# 附件600篇候选复审任务

范围：三个文本附件中的600个候选，包含95项带旧模型评分的条目。旧模型意见完整保存、只作参考；不能当作独立评审已完成。现榜论文一旦确认同一身份，应沿用原paper_id并补齐必要评审，不重复开记录。

## 接收与去重

- [x] 三附件完整下载、逐字节保留，并记录SHA-256。
- [x] 解析900条来源记录并建立600候选映射；核实S4/S5的300条一一对应。
- [x] 与batch015现榜345篇比对规范题名、主URL与可提取文献标识符，未发现确定重复。
- [ ] 取得360库存缺失的15项，完成剩余身份比对；附件自述先前去重不能代替本次证据。
- [ ] 核验5组近似题名的作者、版本和正式来源；不要仅凭模糊匹配删项。
- [ ] 复核附件所述现榜#125 Fredformer与#307 Amplifier链接错配，不直接照搬旧模型指控。

## 每篇执行门槛

1. 先确认官方题名、作者、年份、赛道、DOI/arXiv和版本，检查更名及同文多版；确认重复时映射原paper_id。
2. 下载官方或授权全文、附录、代码及所需数据，记录URL、版本、哈希与实际阅读范围。旧下载失败3项重新查可取得的正式来源；不把失败解释成质量差。
3. 按现行统一量表开展新的独立评审；95项旧评分仅放参考材料区，盲评阶段不当作答案。逐项证据、反例适用条件、NA/unknown边界和版本差异必须可追溯。
4. 将领域专用实验与通用时序能力分开，区分预测、分类、异常、因果、表征、基准、教程和观点论文；不得把领域数据优势直接推广到通用时序。
5. 保留基础模型，额外登记从头训练、微调和推理的复现门槛、数据/权重可得性、GPU/显存/卡时、API费用及数据泄漏风险。
6. 完成独立校验和聚合门槛后再纳入排名；低引用或未知引用不排除。未评审项不能有伪造的新评分。

## 执行队列（600项，全部待新独立评审）

ATT编号仅为本次接收ID，不是已有网站paper_id，不得直接覆盖001–360。

- [ ] ATT-0001 [RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms](https://doi.org/10.1145/3637528.3672062)。来源：S4-001, S5-001。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0002 [Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift](https://doi.org/10.1145/3637528.3671926)。来源：S4-002, S5-002。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0003 [SolarCube: An Integrative Benchmark Dataset Harnessing Satellite and In-situ Observations for Large-scale Solar Energy Forecasting](https://proceedings.neurips.cc/paper_files/paper/2024/hash/06477eb61ea6b85c6608d42a222462df-Abstract-Datasets_and_Benchmarks_Track.html)。来源：S4-003, S5-003。领域专用；energy_solar。
- [ ] ATT-0004 [Multi-Patch Prediction: Adapting Language Models for Time Series Representation Learning](https://proceedings.mlr.press/v235/bian24a.html)。来源：S4-004, S5-004。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源；旧评分仅参考。
- [ ] ATT-0005 [Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling](https://proceedings.neurips.cc/paper_files/paper/2024/hash/298c3e32d7d402189444be2ff5d19979-Abstract-Conference.html)。来源：S4-005, S5-005。领域专用；weather_climate；旧评分仅参考。
- [ ] ATT-0006 [FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting](https://doi.org/10.1145/3637528.3671509)。来源：S4-006, S5-006。领域专用；energy_solar；旧评分仅参考。
- [ ] ATT-0007 [Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/27768)。来源：S4-007, S5-007。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0008 [Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask](https://doi.org/10.1145/3637528.3671673)。来源：S4-008, S5-008。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0009 [Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values](https://proceedings.iclr.cc/paper_files/paper/2024/hash/7f3b25e39e0f0330435d1ffc1d6fa193-Abstract-Conference.html)。来源：S4-009, S5-009。通用时序候选（待全文确认）。
- [ ] ATT-0010 [STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction](https://proceedings.iclr.cc/paper_files/paper/2024/hash/832b20b65f655587e9c0447860406a82-Abstract-Conference.html)。来源：S4-010, S5-010。通用时序候选（待全文确认）。
- [ ] ATT-0011 [Reservoir Computing for Short High-Dimensional Time Series: an Application to SARS-CoV-2 Hospitalization Forecast](https://proceedings.mlr.press/v235/ferte24a.html)。来源：S4-011, S5-011。领域专用；health_biomedicine。
- [ ] ATT-0012 [Efficient and Effective Time-Series Forecasting with Spiking Neural Networks](https://proceedings.mlr.press/v235/lv24d.html)。来源：S4-012, S5-012。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0013 [Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization](https://proceedings.mlr.press/v235/liu24aw.html)。来源：S4-013, S5-013。通用时序候选（待全文确认）。
- [ ] ATT-0014 [Interpretable Sparse System Identification: Beyond Recent Deep Learning Techniques on Time-Series Prediction](https://proceedings.iclr.cc/paper_files/paper/2024/hash/3304c2bbd18129a3cfcef5ea886db4a7-Abstract-Conference.html)。来源：S4-014, S5-014。通用时序候选（待全文确认）。
- [ ] ATT-0015 [Towards Transparent Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2024/hash/b5414313268287725e91a8eae9ddea01-Abstract-Conference.html)。来源：S4-015, S5-015。通用时序候选（待全文确认）。
- [ ] ATT-0016 [GraFITi: Graphs for Forecasting Irregularly Sampled Time Series](https://ojs.aaai.org/index.php/AAAI/article/view/29560)。来源：S4-016, S5-016。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0017 [Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting](https://doi.org/10.1145/3637528.3671961)。来源：S4-017, S5-017。时空（领域待核）；旧评分仅参考。
- [ ] ATT-0018 [Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/29483)。来源：S4-018, S5-018。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0019 [MARLP: Time-series Forecasting Control for Agricultural Managed Aquifer Recharge](https://doi.org/10.1145/3637528.3671533)。来源：S4-019, S5-019。领域专用；environment_agriculture；旧评分仅参考。
- [ ] ATT-0020 [FRNet: Frequency-based Rotation Network for Long-term Time Series Forecasting](https://doi.org/10.1145/3637528.3671713)。来源：S4-020, S5-020。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0021 [Explainable and Interpretable Forecasts on Non-Smooth Multivariate Time Series for Responsible Gameplay](https://doi.org/10.1145/3637528.3671657)。来源：S4-021, S5-021。领域专用；gameplay。
- [ ] ATT-0022 [CGS-Mask: Making Time Series Predictions Intuitive for All](https://ojs.aaai.org/index.php/AAAI/article/view/29325)。来源：S4-022, S5-022。通用时序候选（待全文确认）。
- [ ] ATT-0023 [GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing](https://doi.org/10.1145/3637528.3672055)。来源：S4-023, S5-023。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0024 [Generative Pretrained Hierarchical Transformer for Time Series Forecasting](https://doi.org/10.1145/3637528.3671855)。来源：S4-024, S5-024。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源；旧评分仅参考；与现榜近似题名待核。
- [ ] ATT-0025 [Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Networks](https://doi.org/10.1145/3637528.3671665)。来源：S4-025, S5-025。领域专用；traffic_urban；旧评分仅参考。
- [ ] ATT-0026 [AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting](https://doi.org/10.1145/3637528.3672057)。来源：S4-026, S5-026。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0027 [Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization](https://doi.org/10.1145/3637528.3671969)。来源：S4-027, S5-027。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0028 [Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity](https://doi.org/10.1145/3637528.3671632)。来源：S4-028, S5-028。领域专用；industry_infrastructure；旧评分仅参考。
- [ ] ATT-0029 [Quantifying and Estimating the Predictability Upper Bound of Univariate Numeric Time Series](https://doi.org/10.1145/3637528.3671995)。来源：S4-029, S5-029。通用时序候选（待全文确认）。
- [ ] ATT-0030 [Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling](https://proceedings.mlr.press/v235/marisca24a.html)。来源：S4-030, S5-030。时空（领域待核）；旧评分仅参考。
- [ ] ATT-0031 [Towards a Self-contained Data-driven Global Weather Forecasting Framework](https://proceedings.mlr.press/v235/xiao24a.html)。来源：S4-031, S5-031。领域专用；weather_climate。
- [ ] ATT-0032 [ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/28753)。来源：S4-032, S5-032。领域专用；traffic_urban；旧评分仅参考。
- [ ] ATT-0033 [Spatio-Temporal Pivotal Graph Neural Networks for Traffic Flow Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/28707)。来源：S4-033, S5-033。领域专用；traffic_urban；旧评分仅参考。
- [ ] ATT-0034 [Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations](https://doi.org/10.1145/3589334.3645688)。来源：S4-034, S5-034。领域专用；traffic_urban；旧评分仅参考。
- [ ] ATT-0035 [STEMO: Early Spatio-temporal Forecasting with Multi-Objective Reinforcement Learning](https://doi.org/10.1145/3637528.3671922)。来源：S4-035, S5-035。时空（领域待核）；旧评分仅参考。
- [ ] ATT-0036 [IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark](https://proceedings.neurips.cc/paper_files/paper/2024/hash/f5c7fb98367440a5eb01d70294c180ca-Abstract-Datasets_and_Benchmarks_Track.html)。来源：S4-036, S5-036。通用时序候选（待全文确认）。
- [ ] ATT-0037 [Class-incremental Learning for Time Series: Benchmark and Evaluation](https://doi.org/10.1145/3637528.3671581)。来源：S4-037, S5-037。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0038 [Disentangling Time Series Representations via Contrastive Independence-of-Support on l-Variational Inference](https://proceedings.iclr.cc/paper_files/paper/2024/hash/099ba28b20462dac7bf057d21c27a011-Abstract-Conference.html)。来源：S4-038, S5-038。通用时序候选（待全文确认）。
- [ ] ATT-0039 [T-Rep: Representation Learning for Time Series using Time-Embeddings](https://proceedings.iclr.cc/paper_files/paper/2024/hash/84b946c4c4162b29464fc7aa57e480e1-Abstract-Conference.html)。来源：S4-039, S5-039。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0040 [The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark](https://proceedings.neurips.cc/paper_files/paper/2024/hash/c3f3c690b7a99fba16d0efd35cb83b2c-Abstract-Datasets_and_Benchmarks_Track.html)。来源：S4-040, S5-040。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0041 [Learning diverse causally emergent representations from time series data](https://proceedings.neurips.cc/paper_files/paper/2024/hash/d8398f4da88975e2a9c62ecaa5ba267b-Abstract-Conference.html)。来源：S4-041, S5-041。通用时序候选（待全文确认）。
- [ ] ATT-0042 [Segment, Shuffle, and Stitch: A Simple Layer for Improving Time-Series Representations](https://proceedings.neurips.cc/paper_files/paper/2024/hash/0898f05f6c1d247be3eab8da93d33da1-Abstract-Conference.html)。来源：S4-042, S5-042。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0043 [Exploiting Representation Curvature for Boundary Detection in Time Series](https://proceedings.neurips.cc/paper_files/paper/2024/hash/0b7f639ef28a9035a71f7e0c04c1d681-Abstract-Conference.html)。来源：S4-043, S5-043。通用时序候选（待全文确认）。
- [ ] ATT-0044 [UniMTS: Unified Pre-training for Motion Time Series](https://proceedings.neurips.cc/paper_files/paper/2024/hash/c290d4373c495b2cad0625d6288260f0-Abstract-Conference.html)。来源：S4-044, S5-044。领域专用；motion_activity_sensing；预训练/大模型依赖线索，须核资源；旧评分仅参考。
- [ ] ATT-0045 [TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series](https://proceedings.iclr.cc/paper_files/paper/2024/hash/a4352e2c9d93582898a2a20e1f514e8f-Abstract-Conference.html)。来源：S4-045, S5-045。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源；旧评分仅参考。
- [ ] ATT-0046 [Tri-Level Navigator: LLM-Empowered Tri-Level Learning for Time Series OOD Generalization](https://proceedings.neurips.cc/paper_files/paper/2024/hash/c78d9d035215039ab46e48c281e6a63d-Abstract-Conference.html)。来源：S4-046, S5-046。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源；旧评分仅参考。
- [ ] ATT-0047 [Task-oriented Time Series Imputation Evaluation via Generalized Representers](https://proceedings.neurips.cc/paper_files/paper/2024/hash/f88264fcc54775ee1706116e90fe351a-Abstract-Conference.html)。来源：S4-047, S5-047。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0048 [Large Pre-trained time series models for cross-domain Time series analysis tasks](https://proceedings.neurips.cc/paper_files/paper/2024/hash/662dcc5c2b9aca77b2a0ec8a98aefae9-Abstract-Conference.html)。来源：S4-048, S5-048。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源；旧评分仅参考。
- [ ] ATT-0049 [MF-CLR: Multi-Frequency Contrastive Learning Representation for Time Series](https://proceedings.mlr.press/v235/duan24b.html)。来源：S4-049, S5-049。通用时序候选（待全文确认）。
- [ ] ATT-0050 [TimesURL: Self-Supervised Contrastive Learning for Universal Time Series Representation Learning](https://ojs.aaai.org/index.php/AAAI/article/view/29299)。来源：S4-050, S5-050。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0051 [PATE: Proximity-Aware Time Series Anomaly Evaluation](https://doi.org/10.1145/3637528.3671971)。来源：S4-051, S5-051。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0052 [Vector Quantization Pretraining for EEG Time Series with Random Projection and Phase Alignment](https://proceedings.mlr.press/v235/gui24a.html)。来源：S4-052, S5-052。领域专用；health_biomedicine；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0053 [ChronoEpilogi: Scalable Time Series Selection with Multiple Solutions](https://proceedings.neurips.cc/paper_files/paper/2024/hash/f24e8cc1c1c06a689850ee766a7357b2-Abstract-Conference.html)。来源：S4-053, S5-053。通用时序候选（待全文确认）。
- [ ] ATT-0054 [Reinforced Cross-Domain Knowledge Distillation on Time Series Data](https://proceedings.neurips.cc/paper_files/paper/2024/hash/5424f6b74f475ad738b54888d609283a-Abstract-Conference.html)。来源：S4-054, S5-054。通用时序候选（待全文确认）。
- [ ] ATT-0055 [Parametric Augmentation for Time Series Contrastive Learning](https://proceedings.iclr.cc/paper_files/paper/2024/hash/ccf6d8b4a1fe9d9c8192f00c713872ea-Abstract-Conference.html)。来源：S4-055, S5-055。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0056 [Addressing Spatial-Temporal Heterogeneity: General Mixed Time Series Analysis via Latent Continuity Recovery and Alignment](https://proceedings.neurips.cc/paper_files/paper/2024/hash/1feb87871436031bdc0f2beaa62a049b-Abstract-Conference.html)。来源：S4-056, S5-056。时空（领域待核）；旧评分仅参考。
- [ ] ATT-0057 [Learning Causal Relations from Subsampled Time Series with Two Time-Slices](https://proceedings.mlr.press/v235/wu24p.html)。来源：S4-057, S5-057。通用时序候选（待全文确认）。
- [ ] ATT-0058 [Rough Transformers: Lightweight and Continuous Time Series Modelling through Signature Patching](https://proceedings.neurips.cc/paper_files/paper/2024/hash/bfe167fee3be2d862a56af82dee77720-Abstract-Conference.html)。来源：S4-058, S5-058。通用时序候选（待全文确认）。
- [ ] ATT-0059 [Boosting Transferability and Discriminability for Time Series Domain Adaptation](https://proceedings.neurips.cc/paper_files/paper/2024/hash/b61da4f02b271cb7b5e3d538e2b78fb9-Abstract-Conference.html)。来源：S4-059, S5-059。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0060 [Shape analysis for time series](https://proceedings.neurips.cc/paper_files/paper/2024/hash/ad86418f7bdfa685cd089e028efd75cd-Abstract-Conference.html)。来源：S4-060, S5-060。通用时序候选（待全文确认）。
- [ ] ATT-0061 [Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates](https://doi.org/10.1145/3711896.3737046)。来源：S4-061, S5-061。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0062 [Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time-Series Forecasting Based on Biological ODEs](https://proceedings.iclr.cc/paper_files/paper/2025/hash/565f995643da6329cec701f26f8579f5-Abstract-Conference.html)。来源：S4-062, S5-062。领域专用；health_biomedicine。
- [ ] ATT-0063 [Shifting Time: Time-series Forecasting with Khatri-Rao Neural Operators](https://proceedings.mlr.press/v267/dama25a.html)。来源：S4-063, S5-063。通用时序候选（待全文确认）。
- [ ] ATT-0064 [CSformer: Combining Channel Independence and Mixing for Robust Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/35406)。来源：S4-064, S5-064。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0065 [Neural Conformal Control for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/34029)。来源：S4-065, S5-065。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0066 [Cherry-Picking in Time Series Forecasting: How to Select Datasets to Make Your Model Shine](https://ojs.aaai.org/index.php/AAAI/article/view/34224)。来源：S4-066, S5-066。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0067 [Learning with Calibration: Exploring Test-Time Computing of Spatio-Temporal Forecasting](https://papers.neurips.cc/paper_files/paper/2025/hash/e4a04155055153f18b93824825cd6e3d-Abstract-Conference.html)。来源：S4-067, S5-067。时空（领域待核）。
- [ ] ATT-0068 [Information Bottleneck-guided MLPs for Robust Spatial-temporal Forecasting](https://proceedings.mlr.press/v267/chen25ax.html)。来源：S4-068, S5-068。时空（领域待核）。
- [ ] ATT-0069 [FSTLLM: Spatio-Temporal LLM for Few Shot Time Series Forecasting](https://proceedings.mlr.press/v267/jiang25a.html)。来源：S4-069, S5-069。时空（领域待核）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0070 [TimeCapsule: Solving the Jigsaw Puzzle of Long-Term Time Series Forecasting with Compressed Predictive Representations](https://doi.org/10.1145/3711896.3737157)。来源：S4-070, S5-070。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0071 [When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series](https://proceedings.mlr.press/v267/park25e.html)。来源：S4-071, S5-071。通用时序候选（待全文确认）。
- [ ] ATT-0072 [Investigating Pattern Neurons in Urban Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2025/hash/1fb38e9c776b5ff18511a284570b15c8-Abstract-Conference.html)。来源：S4-072, S5-072。领域专用；traffic_urban。
- [ ] ATT-0073 [Locally Connected Echo State Networks for Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2025/hash/d8bd445c2abe1343cce0e14b361b2fb3-Abstract-Conference.html)。来源：S4-073, S5-073。通用时序候选（待全文确认）。
- [ ] ATT-0074 [TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2025/hash/45bd303ea32a8193d3c19de5d5eaf138-Abstract-Conference.html)。来源：S4-074, S5-074。通用时序候选（待全文确认）。
- [ ] ATT-0075 [LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization](https://proceedings.mlr.press/v267/niu25e.html)。来源：S4-075, S5-075。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0076 [Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting](https://proceedings.mlr.press/v267/schuchardt25a.html)。来源：S4-076, S5-076。通用时序候选（待全文确认）。
- [ ] ATT-0077 [KooNPro: A Variance-Aware Koopman Probabilistic Model Enhanced by Neural Process for Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2025/hash/4846257e355f6923fc2a1fbe35099e91-Abstract-Conference.html)。来源：S4-077, S5-077。通用时序候选（待全文确认）。
- [ ] ATT-0078 [CoMRes: Semi-Supervised Time Series Forecasting Utilizing Consensus Promotion of Multi-Resolution](https://proceedings.iclr.cc/paper_files/paper/2025/hash/6ca155a091aedc939d289df8f16f6c75-Abstract-Conference.html)。来源：S4-078, S5-078。通用时序候选（待全文确认）。
- [ ] ATT-0079 [A Lightweight Sparse Interaction Network for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/33452)。来源：S4-079, S5-079。通用时序候选（待全文确认）。
- [ ] ATT-0080 [Disentangling Long-Short Term State Under Unknown Interventions for Online Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/33717)。来源：S4-080, S5-080。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0081 [Probabilistic Hypergraph Recurrent Neural Networks for Time-series Forecasting](https://doi.org/10.1145/3690624.3709202)。来源：S4-081, S5-081。通用时序候选（待全文确认）。
- [ ] ATT-0082 [CMA: A Unified Contextual Meta-Adaptation Methodology for Time-Series Denoising and Prediction](https://doi.org/10.1145/3711896.3736881)。来源：S4-082, S5-082。通用时序候选（待全文确认）。
- [ ] ATT-0083 [VarDrop: Enhancing Training Efficiency by Reducing Variate Redundancy in Periodic Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/33951)。来源：S4-083, S5-083。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0084 [Times2D: Multi-Period Decomposition and Derivative Mapping for General Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/34164)。来源：S4-084, S5-084。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0085 [Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learning](https://doi.org/10.1145/3711896.3737174)。来源：S4-085, S5-085。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0086 [CrossLinear: Plug-and-Play Cross-Correlation Embedding for Time Series Forecasting with Exogenous Variables](https://doi.org/10.1145/3711896.3736899)。来源：S4-086, S5-086。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0087 [TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents](https://ojs.aaai.org/index.php/AAAI/article/view/33989)。来源：S4-087, S5-087。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源；旧评分仅参考。
- [ ] ATT-0088 [Apollo-Forecast: Overcoming Aliasing and Inference Speed Challenges in Language Models for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/34371)。来源：S4-088, S5-088。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0089 [Quantum Time-index Models with Reservoir for Time Series Forecasting](https://doi.org/10.1145/3690624.3709228)。来源：S4-089, S5-089。通用时序候选（待全文确认）。
- [ ] ATT-0090 [Probabilistic Forecasting of Irregularly Sampled Time Series with Missing Values via Conditional Normalizing Flows](https://ojs.aaai.org/index.php/AAAI/article/view/35494)。来源：S4-090, S5-090。通用时序候选（待全文确认）。
- [ ] ATT-0091 [Test-Time Training with Diversified Local Aggregation Consistency for Mortality Prediction using Clinical Time Series](https://doi.org/10.1145/3711896.3737151)。来源：S4-091, S5-091。领域专用；health_biomedicine。
- [ ] ATT-0092 [Proactive Model Adaptation Against Concept Drift for Online Time Series Forecasting](https://doi.org/10.1145/3690624.3709210)。来源：S4-092, S5-092。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0093 [Sequence Complementor: Complementing Transformers for Time Series Forecasting with Learnable Sequences](https://ojs.aaai.org/index.php/AAAI/article/view/33747)。来源：S4-093, S5-093。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0094 [Timing is important: Risk-aware Fund Allocation based on Time-Series Forecasting](https://doi.org/10.1145/3711896.3737268)。来源：S4-094, S5-094。领域专用；finance。
- [ ] ATT-0095 [Unlocking the Power of LSTM for Long Term Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/33303)。来源：S4-095, S5-095。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0096 [ST-MTM: Masked Time Series Modeling with Seasonal-Trend Decomposition for Time Series Forecasting](https://doi.org/10.1145/3690624.3709254)。来源：S4-096, S5-096。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0097 [Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/33908)。来源：S4-097, S5-097。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0098 [Hierarchical Classification Auxiliary Network for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/34286)。来源：S4-098, S5-098。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0099 [Semantics-Aware Patch Encoding and Hierarchical Dependency Modeling for Long-Term Time Series Forecasting](https://doi.org/10.1145/3711896.3737123)。来源：S4-099, S5-099。通用时序候选（待全文确认）；旧下载失败，待重取。
- [ ] ATT-0100 [xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decomposition](https://ojs.aaai.org/index.php/AAAI/article/view/34270)。来源：S4-100, S5-100。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0101 [SDE: A Simplified and Disentangled Dependency Encoding Framework for State Space Models in Time Series Forecasting](https://doi.org/10.1145/3711896.3737119)。来源：S4-101, S5-101。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0102 [FilterTS: Comprehensive Frequency Filtering for Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/35438)。来源：S4-102, S5-102。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0103 [WaveletMixer: A Multi-Resolution Wavelets Based MLP-Mixer for Multivariate Long-Term Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/34434)。来源：S4-103, S5-103。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0104 [IN-Flow: Instance Normalization Flow for Non-stationary Time Series Forecasting](https://doi.org/10.1145/3690624.3709260)。来源：S4-104, S5-104。通用时序候选（待全文确认）；旧评分仅参考；与现榜近似题名待核。
- [ ] ATT-0105 [HyperMixer: Specializable Hypergraph Channel Mixing for Long-term Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/34302)。来源：S4-105, S5-105。通用时序候选（待全文确认）。
- [ ] ATT-0106 [CAMEF: Causal-Augmented Multi-Modality Event-Driven Financial Forecasting by Integrating Time Series Patterns and Salient Macroeconomic Announcements](https://doi.org/10.1145/3711896.3736872)。来源：S4-106, S5-106。领域专用；finance；旧评分仅参考。
- [ ] ATT-0107 [Beyond Fixed Variables: Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning](https://doi.org/10.1145/3711896.3736854)。来源：S4-107, S5-107。时空（领域待核）。
- [ ] ATT-0108 [Revisiting Attention for Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/35455)。来源：S4-108, S5-108。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0109 [Stochastic Diffusion: A Diffusion Based Model for Stochastic Time Series Forecasting](https://doi.org/10.1145/3711896.3737137)。来源：S4-109, S5-109。通用时序候选（待全文确认）。
- [ ] ATT-0110 [HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/32057)。来源：S4-110, S5-110。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0111 [Multi-period Learning for Financial Time Series Forecasting](https://doi.org/10.1145/3690624.3709422)。来源：S4-111, S5-111。领域专用；finance；旧评分仅参考。
- [ ] ATT-0112 [Exploiting Language Power for Time Series Forecasting with Exogenous Variables](https://doi.org/10.1145/3696410.3714793)。来源：S4-112, S5-112。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0113 [Utilizing Strategic Pre-training to Reduce Overfitting: Baguan – A Pre-trained Weather Forecasting Model](https://doi.org/10.1145/3711896.3737178)。来源：S4-113, S5-113。领域专用；weather_climate；预训练/大模型依赖线索，须核资源；旧评分仅参考。
- [ ] ATT-0114 [ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/33465)。来源：S4-114, S5-114。时空（领域待核）；旧评分仅参考。
- [ ] ATT-0115 [Continuous Ensemble Weather Forecasting with Diffusion models](https://proceedings.iclr.cc/paper_files/paper/2025/hash/424403b4d2553f16d8ad11635a8214b3-Abstract-Conference.html)。来源：S4-115, S5-115。领域专用；weather_climate；旧评分仅参考。
- [ ] ATT-0116 [Mesh Interpolation Graph Network for Dynamic and Spatially Irregular Global Weather Forecasting](https://papers.nips.cc/paper_files/paper/2025/hash/23cf4f3fd33c2fb071fc40aee0ec2884-Abstract-Conference.html)。来源：S4-116, S5-116。领域专用；weather_climate。
- [ ] ATT-0117 [DAWP: A framework for global observation forecasting via Data Assimilation and Weather Prediction in satellite observation space](https://papers.nips.cc/paper_files/paper/2025/hash/da8a39bc39ae1c89dd6ebb1e3bcbb3f3-Abstract-Conference.html)。来源：S4-117, S5-117。领域专用；weather_climate。
- [ ] ATT-0118 [Decision-aware Training of Spatiotemporal Forecasting Models to Select a Top-K Subset of Sites for Intervention](https://proceedings.mlr.press/v267/heuton25a.html)。来源：S4-118, S5-118。时空（领域待核）。
- [ ] ATT-0119 [HR-Extreme: A High-Resolution Dataset for Extreme Weather Forecasting](https://proceedings.iclr.cc/paper_files/paper/2025/hash/3236f661fc6f00e8b16ff42db99acaaf-Abstract-Conference.html)。来源：S4-119, S5-119。领域专用；weather_climate。
- [ ] ATT-0120 [TCP-Diffusion: A Multi-modal Diffusion Model for Global Tropical Cyclone Precipitation Forecasting with Change Awareness](https://proceedings.mlr.press/v267/huang25y.html)。来源：S4-120, S5-120。领域专用；weather_climate。
- [ ] ATT-0121 [LoRA-EnVar: Parameter-Efficient Hybrid Ensemble Variational Assimilation for Weather Forecasting](https://papers.nips.cc/paper_files/paper/2025/hash/413885e70482b95dcbeeddc1daf39177-Abstract-Conference.html)。来源：S4-121, S5-121。领域专用；weather_climate。
- [ ] ATT-0122 [FlowMixer: A Depth-Agnostic Neural Architecture for Interpretable Spatiotemporal Forecasting](https://papers.neurips.cc/paper_files/paper/2025/hash/803485352e61e3ebf41221e4776c9fd4-Abstract-Conference.html)。来源：S4-122, S5-122。时空（领域待核）。
- [ ] ATT-0123 [Less but More: Linear Adaptive Graph Learning Empowering Spatiotemporal Forecasting](https://papers.neurips.cc/paper_files/paper/2025/hash/54c9bfb0885ae07f23607f617ab64c2b-Abstract-Conference.html)。来源：S4-123, S5-123。时空（领域待核）；旧评分仅参考。
- [ ] ATT-0124 [Fixing the Double Penalty in Data-Driven Weather Forecasting Through a Modified Spherical Harmonic Loss Function](https://proceedings.mlr.press/v267/subich25a.html)。来源：S4-124, S5-124。领域专用；weather_climate；旧评分仅参考。
- [ ] ATT-0125 [OmniCast: A Masked Latent Diffusion Model for Weather Forecasting Across Time Scales](https://papers.nips.cc/paper_files/paper/2025/hash/fb0b57dfb8686c357f96e41b880ce7ed-Abstract-Conference.html)。来源：S4-125, S5-125。领域专用；weather_climate。
- [ ] ATT-0126 [RadarQA: Multi-modal Quality Analysis of Weather Radar Forecasts](https://papers.nips.cc/paper_files/paper/2025/hash/beb667eee2fecb594dc3154950ef7aa4-Abstract-Conference.html)。来源：S4-126, S5-126。领域专用；weather_climate。
- [ ] ATT-0127 [Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting](https://proceedings.iclr.cc/paper_files/paper/2025/hash/cb2266111eadcfa2c02187ace64e2183-Abstract-Conference.html)。来源：S4-127, S5-127。时空（领域待核）。
- [ ] ATT-0128 [STKOpt: Automated Spatio-Temporal Knowledge Optimization for Traffic Prediction](https://doi.org/10.1145/3696410.3714598)。来源：S4-128, S5-128。领域专用；traffic_urban；旧下载失败，待重取。
- [ ] ATT-0129 [Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting](https://doi.org/10.1145/3711896.3737081)。来源：S4-129, S5-129。领域专用；weather_climate；旧评分仅参考。
- [ ] ATT-0130 [NRFormer: Nationwide Nuclear Radiation Forecasting with Spatio-Temporal Transformer](https://doi.org/10.1145/3711896.3737252)。来源：S4-130, S5-130。领域专用；energy_solar。
- [ ] ATT-0131 [AutoSTF: Decoupled Neural Architecture Search for Cost-Effective Automated Spatio-Temporal Forecasting](https://doi.org/10.1145/3690624.3709323)。来源：S4-131, S5-131。时空（领域待核）；旧评分仅参考。
- [ ] ATT-0132 [ASTNet: Asynchronous Spatio-Temporal Network for Large-Scale Chemical Sensor Forecasting](https://doi.org/10.1145/3711896.3737194)。来源：S4-132, S5-132。领域专用；industry_infrastructure。
- [ ] ATT-0133 [Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective](https://doi.org/10.1145/3690624.3709177)。来源：S4-133, S5-133。领域专用；traffic_urban；旧评分仅参考。
- [ ] ATT-0134 [Decomposed Spatio-Temporal Mamba for Long-Term Traffic Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/33281)。来源：S4-134, S5-134。领域专用；traffic_urban；旧评分仅参考。
- [ ] ATT-0135 [Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach](https://ojs.aaai.org/index.php/AAAI/article/view/33328)。来源：S4-135, S5-135。时空（领域待核）；旧评分仅参考。
- [ ] ATT-0136 [EWMoE: An Effective Model for Global Weather Forecasting with Mixture-of-Experts](https://ojs.aaai.org/index.php/AAAI/article/view/31997)。来源：S4-136, S5-136。领域专用；weather_climate；旧评分仅参考。
- [ ] ATT-0137 [ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data](https://ojs.aaai.org/index.php/AAAI/article/view/33310)。来源：S4-137, S5-137。时空（领域待核）；旧评分仅参考。
- [ ] ATT-0138 [Drawing Informative Gradients from Sources: A One-stage Transfer Learning Framework for Cross-city Spatiotemporal Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/32102)。来源：S4-138, S5-138。领域专用；traffic_urban；旧评分仅参考。
- [ ] ATT-0139 [Spatial-Temporal Heterogenous Graph Contrastive Learning for Microservice Workload Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/33271)。来源：S4-139, S5-139。领域专用；industry_infrastructure。
- [ ] ATT-0140 [DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting](https://doi.org/10.1145/3690624.3709391)。来源：S4-140, S5-140。时空（领域待核）；旧评分仅参考。
- [ ] ATT-0141 [Does Vector Quantization Fail in Spatio-Temporal Forecasting? Exploring a Differentiable Sparse Soft-Vector Quantization Approach](https://doi.org/10.1145/3711896.3736914)。来源：S4-141, S5-141。时空（领域待核）。
- [ ] ATT-0142 [Efficient Traffic Prediction Through Spatio-Temporal Distillation](https://ojs.aaai.org/index.php/AAAI/article/view/32096)。来源：S4-142, S5-142。领域专用；traffic_urban；旧评分仅参考。
- [ ] ATT-0143 [FairTP: A Prolonged Fairness Framework for Traffic Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/34838)。来源：S4-143, S5-143。领域专用；traffic_urban。
- [ ] ATT-0144 [UoMo: A Universal Model of Mobile Traffic Forecasting for Wireless Network Optimization](https://doi.org/10.1145/3711896.3737272)。来源：S4-144, S5-144。领域专用；traffic_urban；旧评分仅参考。
- [ ] ATT-0145 [SSL-STMFormer Self-Supervised Learning Spatio-Temporal Entanglement Transformer for Traffic Flow Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/33321)。来源：S4-145, S5-145。领域专用；traffic_urban；旧评分仅参考。
- [ ] ATT-0146 [Toward Interpretable Evaluation Measures for Time Series Segmentation](https://papers.nips.cc/paper_files/paper/2025/hash/09425891e393e64b0535194a81ba15b7-Abstract-Conference.html)。来源：S4-146, S5-146。通用时序候选（待全文确认）。
- [ ] ATT-0147 [CausalRivers - Scaling up benchmarking of causal discovery for real-world time-series](https://proceedings.iclr.cc/paper_files/paper/2025/hash/a205fda871b0f6c1e18a7ad7325eb6cf-Abstract-Conference.html)。来源：S4-147, S5-147。通用时序候选（待全文确认）。
- [ ] ATT-0148 [Real-Time Calibration Model for Low-Cost Sensor in Fine-Grained Time Series](https://ojs.aaai.org/index.php/AAAI/article/view/31974)。来源：S4-148, S5-148。领域专用；industry_infrastructure。
- [ ] ATT-0149 [Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation](https://doi.org/10.1145/3690624.3709239)。来源：S4-149, S5-149。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0150 [Learning Without Augmenting: Unsupervised Time Series Representation Learning via Frame Projections](https://papers.nips.cc/paper_files/paper/2025/hash/b05616c9f202380cd3075ddabb6bf86d-Abstract-Conference.html)。来源：S4-150, S5-150。通用时序候选（待全文确认）。
- [ ] ATT-0151 [AdaTS: Learning Adaptive Time Series Representations via Dynamic Soft Contrasts](https://papers.nips.cc/paper_files/paper/2025/hash/6c66efb65c3031bd09b2201ca9a28a74-Abstract-Conference.html)。来源：S4-151, S5-151。通用时序候选（待全文确认）。
- [ ] ATT-0152 [Time Series Representations with Hard-Coded Invariances](https://proceedings.mlr.press/v267/germain25a.html)。来源：S4-152, S5-152。通用时序候选（待全文确认）。
- [ ] ATT-0153 [Unleashing The Power of Pre-Trained Language Models for Irregularly Sampled Time Series](https://doi.org/10.1145/3711896.3737171)。来源：S4-153, S5-153。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源；旧评分仅参考。
- [ ] ATT-0154 [FAT: Frequency-Aware Pretraining for Enhanced Time-Series Representation Learning](https://doi.org/10.1145/3711896.3736952)。来源：S4-154, S5-154。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源；旧下载失败，待重取。
- [ ] ATT-0155 [Pre-training Time Series Models with Stock Data Customization](https://doi.org/10.1145/3711896.3737005)。来源：S4-155, S5-155。领域专用；finance；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0156 [Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals](https://doi.org/10.1145/3696410.3714931)。来源：S4-156, S5-156。领域专用；motion_activity_sensing；旧评分仅参考。
- [ ] ATT-0157 [Federated Foundation Models on Heterogeneous Time Series](https://ojs.aaai.org/index.php/AAAI/article/view/33739)。来源：S4-157, S5-157。通用时序候选（待全文确认）；基础模型线索，须核复现门槛；旧评分仅参考。
- [ ] ATT-0158 [Frequency-Masked Embedding Inference: A Non-Contrastive Approach for Time Series Representation Learning](https://ojs.aaai.org/index.php/AAAI/article/view/33828)。来源：S4-158, S5-158。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0159 [STEM-LTS: Integrating Semantic-Temporal Dynamics in LLM-driven Time Series Analysis](https://ojs.aaai.org/index.php/AAAI/article/view/34447)。来源：S4-159, S5-159。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源；旧评分仅参考。
- [ ] ATT-0160 [DyCAST: Learning Dynamic Causal Structure from Time Series](https://proceedings.iclr.cc/paper_files/paper/2025/hash/3918dd28e51ce20c38c8a1c05758e4a0-Abstract-Conference.html)。来源：S4-160, S5-160。通用时序候选（待全文确认）。
- [ ] ATT-0161 [Dynamic Dual-Feedback Conformal Inference for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/155680)。来源：S4-161, S5-161。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0162 [Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters](https://neurips.cc/virtual/2026/poster/154722)。来源：S4-162, S5-162。通用时序候选（待全文确认）。
- [ ] ATT-0163 [GlucoFM-Bench: Benchmarking Time-Series Foundation Models for Blood Glucose Forecasting](https://neurips.cc/virtual/2026/poster/139533)。来源：S4-163, S5-163。领域专用；health_biomedicine；基础模型线索，须核复现门槛。
- [ ] ATT-0164 [Differencing the Diffusion Trajectory toward Uncertain Components for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/154330)。来源：S4-164, S5-164。通用时序候选（待全文确认）。
- [ ] ATT-0165 [Nuwa: Evaluation-Grounded Agentic Construction of Time Series Forecasting Systems](https://neurips.cc/virtual/2026/poster/153330)。来源：S4-165, S5-165。通用时序候选（待全文确认）。
- [ ] ATT-0166 [FISC: Time-Series Forecasting via First-Layer Statistical Calibration Constraints](https://neurips.cc/virtual/2026/poster/149568)。来源：S4-166, S5-166。通用时序候选（待全文确认）。
- [ ] ATT-0167 [Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach](https://neurips.cc/virtual/2026/poster/149487)。来源：S4-167, S5-167。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0168 [Robust Residual Correction via Selective Deployment for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/154644)。来源：S4-168, S5-168。通用时序候选（待全文确认）。
- [ ] ATT-0169 [PAMod: Modeling Cyclical Shifts via Phase-Amplitude Modulation for Non-stationary Time Series Forecasting](https://neurips.cc/virtual/2026/poster/155815)。来源：S4-169, S5-169。通用时序候选（待全文确认）。
- [ ] ATT-0170 [Evaluating Uncertainty Calibration in Probabilistic Time Series Foundation Models](https://neurips.cc/virtual/2026/poster/138974)。来源：S4-170, S5-170。通用时序候选（待全文确认）；基础模型线索，须核复现门槛；旧评分仅参考。
- [ ] ATT-0171 [PhyTS: A Benchmark for Scientific Time Series](https://neurips.cc/virtual/2026/poster/139305)。来源：S4-171, S5-171。通用时序候选（待全文确认）。
- [ ] ATT-0172 [Revisiting the Generic Transformer: Deconstructing a Strong Baseline for Time Series Foundation Models](https://neurips.cc/virtual/2026/poster/155883)。来源：S4-172, S5-172。通用时序候选（待全文确认）；基础模型线索，须核复现门槛。
- [ ] ATT-0173 [Why Do Time Series Models Need Long Context Windows?](https://neurips.cc/virtual/2026/poster/149819)。来源：S4-173, S5-173。通用时序候选（待全文确认）。
- [ ] ATT-0174 [Filter Banks: from Low-Rank Representations to Deep Models for Efficient Time Series Forecasting](https://neurips.cc/virtual/2026/poster/152048)。来源：S4-174, S5-174。通用时序候选（待全文确认）。
- [ ] ATT-0175 [When to Trust Memory: Retrieval-Guided Probabilistic Spatiotemporal Forecasting under Distribution Shift](https://neurips.cc/virtual/2026/poster/151106)。来源：S4-175, S5-175。时空（领域待核）。
- [ ] ATT-0176 [OrangeTree: A Linear and Tree-based Time Series Forecasting Model Supporting Multiple Input and Output Lengths](https://neurips.cc/virtual/2026/poster/151255)。来源：S4-176, S5-176。通用时序候选（待全文确认）。
- [ ] ATT-0177 [CVTA: Cross-Variable Temporal Attention for Multivariate Irregular Time Series Prediction](https://neurips.cc/virtual/2026/poster/152025)。来源：S4-177, S5-177。通用时序候选（待全文确认）。
- [ ] ATT-0178 [TimeES: Probabilistic and Deterministic Time Series Forecasting via Evolutionary Spectra](https://neurips.cc/virtual/2026/poster/154967)。来源：S4-178, S5-178。通用时序候选（待全文确认）。
- [ ] ATT-0179 [BucpTSF: Breaking the Uniform Computation Paradigm in Time-Series Forecasting](https://neurips.cc/virtual/2026/poster/148238)。来源：S4-179, S5-179。通用时序候选（待全文确认）。
- [ ] ATT-0180 [Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting](https://neurips.cc/virtual/2026/poster/149780)。来源：S4-180, S5-180。领域专用；finance。
- [ ] ATT-0181 [OTformer: Non-Stationarity-Aware Adaptive Optimal Transport Attention for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/148743)。来源：S4-181, S5-181。通用时序候选（待全文确认）。
- [ ] ATT-0182 [HAMSTAR: Hamiltonian Structured Inter-Period Refinement for Long-Term Time Series Forecasting](https://neurips.cc/virtual/2026/poster/150931)。来源：S4-182, S5-182。通用时序候选（待全文确认）。
- [ ] ATT-0183 [Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting](https://neurips.cc/virtual/2026/poster/154424)。来源：S4-183, S5-183。通用时序候选（待全文确认）。
- [ ] ATT-0184 [TStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting](https://neurips.cc/virtual/2026/poster/149443)。来源：S4-184, S5-184。通用时序候选（待全文确认）。
- [ ] ATT-0185 [Predictive Surprise as Self-Grounding Concept Bottleneck for Interpretable Time Series](https://neurips.cc/virtual/2026/poster/153183)。来源：S4-185, S5-185。通用时序候选（待全文确认）。
- [ ] ATT-0186 [Beyond Isolation: Neighbor-Consistent Data Pruning for Multivariate Time Series Forecasting](https://neurips.cc/virtual/2026/poster/154771)。来源：S4-186, S5-186。通用时序候选（待全文确认）。
- [ ] ATT-0187 [Paloma: Phase-Conditioned Residual Modulation for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/154087)。来源：S4-187, S5-187。通用时序候选（待全文确认）。
- [ ] ATT-0188 [FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting](https://neurips.cc/virtual/2026/poster/151862)。来源：S4-188, S5-188。通用时序候选（待全文确认）。
- [ ] ATT-0189 [QWaveNet: Quantum-Enhanced Wavelet Network for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/152829)。来源：S4-189, S5-189。通用时序候选（待全文确认）。
- [ ] ATT-0190 [To See Far, Look Close: Evolutionary Forecasting for Long-term Time Series](https://neurips.cc/virtual/2026/poster/152940)。来源：S4-190, S5-190。通用时序候选（待全文确认）。
- [ ] ATT-0191 [Multivariate Time Series Forecasting needs Cross Variable Loss](https://neurips.cc/virtual/2026/poster/150730)。来源：S4-191, S5-191。通用时序候选（待全文确认）。
- [ ] ATT-0192 [Beyond Linear Decoders: Dynamic Expert-Coupled Optimal Decoding for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/153816)。来源：S4-192, S5-192。通用时序候选（待全文确认）。
- [ ] ATT-0193 [Spectral Energy Allocation Enables Source-Free Domain Adaptation in Time Series Forecasting](https://neurips.cc/virtual/2026/oral/154915)。来源：S4-193, S5-193。通用时序候选（待全文确认）。
- [ ] ATT-0194 [Rethinking Post-Training Recipes for Multimodal Time-Series Forecasting](https://neurips.cc/virtual/2026/poster/153768)。来源：S4-194, S5-194。通用时序候选（待全文确认）。
- [ ] ATT-0195 [Rubato: Signature Attention for Irregular Multivariate Time Series Forecasting](https://neurips.cc/virtual/2026/poster/153092)。来源：S4-195, S5-195。通用时序候选（待全文确认）。
- [ ] ATT-0196 [When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting](https://doi.org/10.1145/3770855.3819018)。来源：S4-196, S5-196。通用时序候选（待全文确认）；基础模型线索，须核复现门槛。
- [ ] ATT-0197 [SEER: Transformer-based Robust Time Series Forecasting via Automated Patch Enhancement and Replacement](https://proceedings.mlr.press/v306/qiu26n.html)。来源：S4-197, S5-197。通用时序候选（待全文确认）。
- [ ] ATT-0198 [Beyond Model Ranking: Predictability-Aligned Evaluation for Time Series Forecasting](https://proceedings.mlr.press/v306/feng26u.html)。来源：S4-198, S5-198。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0199 [Tackling Time-Series Forecasting Generalization via Mitigating Concept Drift](https://proceedings.iclr.cc/paper_files/paper/2026/hash/96d328a1f6d8396d8c8a62f2beee252a-Abstract-Conference.html)。来源：S4-199, S5-199。通用时序候选（待全文确认）。
- [ ] ATT-0200 [Robust Inter-Series Dependency Modeling for Time Series Forecasting via Information-Theoretic Alignment](https://proceedings.mlr.press/v306/yu26n.html)。来源：S4-200, S5-200。通用时序候选（待全文确认）。
- [ ] ATT-0201 [Taming the Recent-Data Bias: Towards Robust Time Series Forecasting with Global Context](https://proceedings.mlr.press/v306/xu26ab.html)。来源：S4-201, S5-201。通用时序候选（待全文确认）。
- [ ] ATT-0202 [Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependency, Asynchrony, and Missingness](https://proceedings.iclr.cc/paper_files/paper/2026/hash/f924dab7a06d0463fe238904678acaee-Abstract-Conference.html)。来源：S4-202, S5-202。通用时序候选（待全文确认）。
- [ ] ATT-0203 [Beyond Point Predictions: Manifold Expansion and Dual Alignment for Robust Time Series Distillation](https://proceedings.mlr.press/v306/hong26e.html)。来源：S4-203, S5-203。通用时序候选（待全文确认）。
- [ ] ATT-0204 [Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting](https://proceedings.mlr.press/v306/han26v.html)。来源：S4-204, S5-204。领域专用；weather_climate。
- [ ] ATT-0205 [DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting](https://proceedings.mlr.press/v306/zhong26l.html)。来源：S4-205, S5-205。通用时序候选（待全文确认）。
- [ ] ATT-0206 [ResCP: Reservoir Conformal Prediction for Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2026/hash/b0a978efeb20d33c15e31a5beee9f6df-Abstract-Conference.html)。来源：S4-206, S5-206。通用时序候选（待全文确认）。
- [ ] ATT-0207 [Beyond Holistic Models: Systematic Component-level Benchmarking of Deep Multivariate Time-Series Forecasting](https://doi.org/10.1145/3770855.3817551)。来源：S4-207, S5-207。通用时序候选（待全文确认）。
- [ ] ATT-0208 [Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration](https://doi.org/10.1145/3770855.3817729)。来源：S4-208, S5-208。通用时序候选（待全文确认）。
- [ ] ATT-0209 [FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance](https://doi.org/10.1145/3770855.3817578)。来源：S4-209, S5-209。领域专用；finance。
- [ ] ATT-0210 [APT: Affine Prototype-Timestamp for Time Series Forecasting Under Distribution Shift](https://ojs.aaai.org/index.php/AAAI/article/view/38542)。来源：S4-210, S5-210。通用时序候选（待全文确认）。
- [ ] ATT-0211 [Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventions](https://doi.org/10.1145/3770855.3817522)。来源：S4-211, S5-211。领域专用；health_biomedicine。
- [ ] ATT-0212 [SEED: Spectral Entropy-Guided Evaluation of Spatial-Temporal Dependencies for Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/39930)。来源：S4-212, S5-212。时空（领域待核）。
- [ ] ATT-0213 [ReCast: Reliability-aware Codebook-assisted Lightweight Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/39610)。来源：S4-213, S5-213。通用时序候选（待全文确认）。
- [ ] ATT-0214 [Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecasting](https://proceedings.mlr.press/v306/moretti26a.html)。来源：S4-214, S5-214。通用时序候选（待全文确认）；旧评分仅参考。
- [ ] ATT-0215 [TimeMRA: LLM-Empowered Time Series Forecasting via Multi-Scale Retrieval-Augmented Representations](https://proceedings.mlr.press/v306/shang26b.html)。来源：S4-215, S5-215。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0216 [Channel Adapter for Time Series Foundation Models in Zero-Shot Multivariate Forecasting](https://proceedings.mlr.press/v306/li26bu.html)。来源：S4-216, S5-216。通用时序候选（待全文确认）；基础模型线索，须核复现门槛。
- [ ] ATT-0217 [Invariant Representation Learning for Source-Free Time Series Forecasting with LLM-Centric Proxy Denoising](https://proceedings.mlr.press/v306/yan26l.html)。来源：S4-217, S5-217。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0218 [See More, Forecast Better and Faster: Enhancing Time Series Foundation Models via Inference-Time Plug-and-Play Downsampling](https://proceedings.mlr.press/v306/xu26ac.html)。来源：S4-218, S5-218。通用时序候选（待全文确认）；基础模型线索，须核复现门槛。
- [ ] ATT-0219 [LagLLM: LLM-empowered lead–lag dependency learning for spatial-temporal time series forecasting](https://proceedings.mlr.press/v306/wu26ci.html)。来源：S4-219, S5-219。时空（领域待核）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0220 [Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2026/hash/410dacbcfda0388f69b8983ed771c44e-Abstract-Conference.html)。来源：S4-220, S5-220。通用时序候选（待全文确认）。
- [ ] ATT-0221 [FireSentry: A Multi-Modal Spatio-temporal Benchmark Dataset for Fine-Grained Wildfire Spread Forecasting](https://doi.org/10.1145/3770854.3785696)。来源：S4-221, S5-221。领域专用；environment_agriculture。
- [ ] ATT-0222 [Pre-training Epidemic Time Series Forecasters with Compartmental Prototypes](https://doi.org/10.1145/3770855.3818928)。来源：S4-222, S5-222。领域专用；health_biomedicine；预训练/大模型依赖线索，须核资源；旧评分仅参考。
- [ ] ATT-0223 [LoFT-LLM: Low-Frequency Time-series Forecasting with Large Language Models](https://doi.org/10.1145/3770854.3780245)。来源：S4-223, S5-223。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0224 [Efficient Test-Time Scaling for LLM-based Time Series Forecasting](https://doi.org/10.1145/3770855.3818201)。来源：S4-224, S5-224。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0225 [Energy-Efficient Training-Free Zero-Inflation Correction for Rainfall Forecasting with Time-Series Foundation Models](https://doi.org/10.1145/3774904.3792998)。来源：S4-225, S5-225。领域专用；weather_climate；基础模型线索，须核复现门槛。
- [ ] ATT-0226 [Unifying Channel Independence and Mixing: Multi-Scale Patch Recursion for Global–Local Representation Synergy in Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/40072)。来源：S4-226, S5-226。通用时序候选（待全文确认）。
- [ ] ATT-0227 [M3Time: LLM-Enhanced Multi-Modal, Multi-Scale, and Multi-Frequency Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/39383)。来源：S4-227, S5-227。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0228 [Unified Spatio-Temporal Tokens are Bases for Generalizable Traffic Forecasting](https://doi.org/10.1145/3770855.3817746)。来源：S4-228, S5-228。领域专用；traffic_urban。
- [ ] ATT-0229 [One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data](https://doi.org/10.1145/3770855.3817960)。来源：S4-229, S5-229。通用时序候选（待全文确认）。
- [ ] ATT-0230 [Scalable Uncertainty Quantification for Extreme Weather Forecasting via Empirical Neural Tangent Kernels](https://doi.org/10.1145/3770855.3818106)。来源：S4-230, S5-230。领域专用；weather_climate。
- [ ] ATT-0231 [GraFT: Infusing Pre-trained Transformers with Relational Structure for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/40029)。来源：S4-231, S5-231。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0232 [Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting](https://doi.org/10.1145/3770855.3818835)。来源：S4-232, S5-232。领域专用；weather_climate；基础模型线索，须核复现门槛。
- [ ] ATT-0233 [RePatch: Learning Entropy-Guided Patch Structures with Quantized Representations for Time Series Forecasting](https://doi.org/10.1145/3770855.3817683)。来源：S4-233, S5-233。通用时序候选（待全文确认）。
- [ ] ATT-0234 [PatchCLE: Breaking the Linear Representation Bottleneck in Time Series Forecasting via Soft Contrastive Learning](https://doi.org/10.1145/3770855.3818023)。来源：S4-234, S5-234。通用时序候选（待全文确认）。
- [ ] ATT-0235 [TimeCAP: A Channel-Aware Pre-Training Framework for Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/39700)。来源：S4-235, S5-235。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0236 [TimeGuard: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting](https://proceedings.mlr.press/v306/nguyen26g.html)。来源：S4-236, S5-236。通用时序候选（待全文确认）。
- [ ] ATT-0237 [Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecasting](https://proceedings.mlr.press/v306/qiu26t.html)。来源：S4-237, S5-237。通用时序候选（待全文确认）。
- [ ] ATT-0238 [L-Drive: Beyond a Single Mapping—Latent Context Drives Time Series Forecasting](https://proceedings.mlr.press/v306/zhang26p.html)。来源：S4-238, S5-238。通用时序候选（待全文确认）。
- [ ] ATT-0239 [Reviving Error Correction in Modern Deep Time-Series Forecasting](https://proceedings.mlr.press/v306/nguyen26u.html)。来源：S4-239, S5-239。通用时序候选（待全文确认）。
- [ ] ATT-0240 [KUMA: A Novel Framework with Koopman Separation and Efficient Multilevel Extraction in Time Series Forecasting](https://proceedings.mlr.press/v306/xiong26j.html)。来源：S4-240, S5-240。通用时序候选（待全文确认）。
- [ ] ATT-0241 [KITE: Knowledge-Guided Probabilistic Modeling for Time Series Forecasting with Exogenous Variables](https://proceedings.mlr.press/v306/cheng26t.html)。来源：S4-241, S5-241。通用时序候选（待全文确认）。
- [ ] ATT-0242 [FACT: Fine-grained Across-variable Convolution for Multivariate Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2026/hash/090d636f951ac0bb18fd5610c95c6a76-Abstract-Conference.html)。来源：S4-242, S5-242。通用时序候选（待全文确认）。
- [ ] ATT-0243 [CPiRi: Channel Permutation-Invariant Relational Interaction for Multivariate Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2026/hash/ebba182cb97864368fdb6ae00773a5e4-Abstract-Conference.html)。来源：S4-243, S5-243。通用时序候选（待全文确认）。
- [ ] ATT-0244 [ConFlux: Multivariate Time Series in Flux, One Unified Forecast in Confluence](https://proceedings.mlr.press/v306/wang26fy.html)。来源：S4-244, S5-244。通用时序候选（待全文确认）。
- [ ] ATT-0245 [Revealing Scaling Paradox in Large-scale Time Series Models: Implications for More Efficient and Accurate Forecasting](https://proceedings.mlr.press/v306/qiu26q.html)。来源：S4-245, S5-245。通用时序候选（待全文确认）；基础模型线索，须核复现门槛。
- [ ] ATT-0246 [Dynamic TMoE: A Drift-Aware Dynamic Mixture of Experts Framework for Non-Stationary Time Series Forecasting](https://proceedings.mlr.press/v306/zhu26ad.html)。来源：S4-246, S5-246。通用时序候选（待全文确认）。
- [ ] ATT-0247 [Routing Channel-Patch Dependencies in Time Series Forecasting with Graph Spectral Decomposition](https://proceedings.iclr.cc/paper_files/paper/2026/hash/494ad6d9b36d9a1528dbf3baeb4e8a72-Abstract-Conference.html)。来源：S4-247, S5-247。通用时序候选（待全文确认）。
- [ ] ATT-0248 [Crisp: A Spectral-Based Interaction Strategy for Multivariate Time Series Forecasting](https://proceedings.mlr.press/v306/wang26ec.html)。来源：S4-248, S5-248。通用时序候选（待全文确认）。
- [ ] ATT-0249 [PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting](https://proceedings.mlr.press/v306/liu26dn.html)。来源：S4-249, S5-249。通用时序候选（待全文确认）。
- [ ] ATT-0250 [From Observations to States: Latent Time Series Forecasting](https://proceedings.mlr.press/v306/yang26y.html)。来源：S4-250, S5-250。通用时序候选（待全文确认）。
- [ ] ATT-0251 [MoRGen: Mixture-of-Resolutions Generative Forecasting for Irregularly Sampled Medical Time-Series Data](https://proceedings.mlr.press/v306/oufattole26a.html)。来源：S4-251, S5-251。领域专用；health_biomedicine。
- [ ] ATT-0252 [COSA: Context-aware Output-Space Adapter for Test-Time Adaptation in Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2026/hash/2a8ce71baac4c89bf9ff479d8240c7d9-Abstract-Conference.html)。来源：S4-252, S5-252。通用时序候选（待全文确认）。
- [ ] ATT-0253 [ReNF: Rethinking the Design of Neural Long-Term Time Series Forecasters](https://proceedings.mlr.press/v306/lu26w.html)。来源：S4-253, S5-253。通用时序候选（待全文确认）。
- [ ] ATT-0254 [StretchTime: Adaptive Time Series Forecasting via Symplectic Attention](https://proceedings.mlr.press/v306/kim26bk.html)。来源：S4-254, S5-254。通用时序候选（待全文确认）。
- [ ] ATT-0255 [Byte Pair Encoding for Efficient Time Series Forecasting](https://proceedings.mlr.press/v306/gotz26a.html)。来源：S4-255, S5-255。通用时序候选（待全文确认）。
- [ ] ATT-0256 [PHAT: Modeling Period Heterogeneity for Multivariate Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2026/hash/dbb5180957513805ebeea787b8c66ac9-Abstract-Conference.html)。来源：S4-256, S5-256。通用时序候选（待全文确认）。
- [ ] ATT-0257 [FIPN: Forward Self-Organizing Interpretable Polynomial Networks for Time Series Forecasting](https://proceedings.mlr.press/v306/wang26hv.html)。来源：S4-257, S5-257。通用时序候选（待全文确认）。
- [ ] ATT-0258 [PESD-TSF: A Period-Aware and Explicit Structured Decomposition Framework for Long-Term Time Series Forecasting](https://proceedings.mlr.press/v306/wang26cs.html)。来源：S4-258, S5-258。通用时序候选（待全文确认）。
- [ ] ATT-0259 [SDHilb: Schrödinger Dynamics-Guided Neural Network with Adaptive Multi-Scale Hilbert Transform for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/152741)。来源：S4-259, S5-259。通用时序候选（待全文确认）。
- [ ] ATT-0260 [DSENet: A Novel Dual-Stream Enhancement Network for Multi-Scale Non-Stationary Time Series Forecasting](https://proceedings.mlr.press/v306/wang26lu.html)。来源：S4-260, S5-260。通用时序候选（待全文确认）。
- [ ] ATT-0261 [Ellipsoidal Time Series Forecasting](https://proceedings.mlr.press/v306/wang26ax.html)。来源：S4-261, S5-261。通用时序候选（待全文确认）。
- [ ] ATT-0262 [Stability-Constrained Regime-Aware Forecasting for Heterogeneous Panel Time Series](https://neurips.cc/virtual/2026/poster/153846)。来源：S4-262, S5-262。通用时序候选（待全文确认）。
- [ ] ATT-0263 [TimeSeed: Effective Time Series Forecasting with Sparse Endogenous Variables](https://proceedings.mlr.press/v306/wu26l.html)。来源：S4-263, S5-263。通用时序候选（待全文确认）。
- [ ] ATT-0264 [Parameter Decorrelation via Transition-Variance Alignment for Multivariate Time-series Forecasting](https://proceedings.mlr.press/v306/choi26j.html)。来源：S4-264, S5-264。通用时序候选（待全文确认）。
- [ ] ATT-0265 [Time–Frequency Non-Stationary Modeling for Multivariate Time Series Forecasting](https://neurips.cc/virtual/2026/poster/152338)。来源：S4-265, S5-265。通用时序候选（待全文确认）。
- [ ] ATT-0266 [Information Geometry Loss for Time Series Forecasting](https://proceedings.mlr.press/v306/fang26m.html)。来源：S4-266, S5-266。通用时序候选（待全文确认）。
- [ ] ATT-0267 [Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting](https://neurips.cc/virtual/2026/poster/148905)。来源：S4-267, S5-267。通用时序候选（待全文确认）。
- [ ] ATT-0268 [Echoes of Error: Residual-Directional Local Rollout Consistency for Timeseries Forecasting](https://neurips.cc/virtual/2026/poster/152212)。来源：S4-268, S5-268。通用时序候选（待全文确认）。
- [ ] ATT-0269 [Numerion: A Multi-Hypercomplex Model for Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2026/hash/5eca2e4fe7858cbbfef4e08573cfcb25-Abstract-Conference.html)。来源：S4-269, S5-269。通用时序候选（待全文确认）。
- [ ] ATT-0270 [Anti-Aliasing Matters: A Dynamic Network for Time Series Forecasting](https://proceedings.mlr.press/v306/zhou26aq.html)。来源：S4-270, S5-270。通用时序候选（待全文确认）。
- [ ] ATT-0271 [Online time series prediction using feature adjustment](https://proceedings.iclr.cc/paper_files/paper/2026/hash/f418594e90047a10f4c158f70d6701cc-Abstract-Conference.html)。来源：S4-271, S5-271。通用时序候选（待全文确认）。
- [ ] ATT-0272 [MixLinear: Extreme Low Resource Multivariate Time Series Forecasting with $0.1K$ Parameters](https://proceedings.iclr.cc/paper_files/paper/2026/hash/7db2348b5bfeca620aa7327df815adcc-Abstract-Conference.html)。来源：S4-272, S5-272。通用时序候选（待全文确认）。
- [ ] ATT-0273 [SpikeSTAG: A Dendritic Compartmental Spiking Graph Network for Multivariate Time-Series Forecasting](https://neurips.cc/virtual/2026/poster/153490)。来源：S4-273, S5-273。通用时序候选（待全文确认）。
- [ ] ATT-0274 [AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/153106)。来源：S4-274, S5-274。通用时序候选（待全文确认）。
- [ ] ATT-0275 [Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval](https://proceedings.iclr.cc/paper_files/paper/2026/hash/7a3284ce53f620d244fca0c79d94c478-Abstract-Conference.html)。来源：S4-275, S5-275。通用时序候选（待全文确认）。
- [ ] ATT-0276 [Not All Frequencies Are Equal: Energy-Adaptive Diffusion for Time Series Forecasting](https://proceedings.mlr.press/v306/qin26e.html)。来源：S4-276, S5-276。通用时序候选（待全文确认）。
- [ ] ATT-0277 [Hi-Time: Hierarchical Latent Prediction for Multivariate Time Series Classification](https://proceedings.mlr.press/v306/zeng26s.html)。来源：S4-277, S5-277。通用时序候选（待全文确认）。
- [ ] ATT-0278 [DistDF: Time-series Forecasting Needs Joint-distribution Wasserstein Alignment](https://proceedings.iclr.cc/paper_files/paper/2026/hash/d591ef4c631b33ac4e08dd88f1ee1dff-Abstract-Conference.html)。来源：S4-278, S5-278。通用时序候选（待全文确认）。
- [ ] ATT-0279 [MDK-MoE: Multi-view Decomposed Kalman Mixture of Experts Framework for Non-stationary Time Series Forecasting](https://neurips.cc/virtual/2026/poster/151139)。来源：S4-279, S5-279。通用时序候选（待全文确认）。
- [ ] ATT-0280 [Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting](https://proceedings.mlr.press/v306/li26fu.html)。来源：S4-280, S5-280。通用时序候选（待全文确认）。
- [ ] ATT-0281 [Are Global Dependencies Necessary? Scalable Time Series Forecasting via Local Cross-Variate Modeling](https://proceedings.iclr.cc/paper_files/paper/2026/hash/a832b65da26c4867a8581cb13d46ad6d-Abstract-Conference.html)。来源：S4-281, S5-281。通用时序候选（待全文确认）。
- [ ] ATT-0282 [IMTS-Tokenizer: Time-Aware Tokenization for Irregular Multivariate Time Series Forecasting](https://neurips.cc/virtual/2026/poster/154661)。来源：S4-282, S5-282。通用时序候选（待全文确认）。
- [ ] ATT-0283 [MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning](https://proceedings.mlr.press/v306/tao26a.html)。来源：S4-283, S5-283。通用时序候选（待全文确认）。
- [ ] ATT-0284 [Nested Spatio-Temporal Time Series Forecasting](https://proceedings.mlr.press/v306/ai26f.html)。来源：S4-284, S5-284。时空（领域待核）。
- [ ] ATT-0285 [MSAR: Next-Scale Autoregressive Forecasting for Time Series via Modular Multi-Scale Decoupling](https://neurips.cc/virtual/2026/poster/149231)。来源：S4-285, S5-285。通用时序候选（待全文确认）。
- [ ] ATT-0286 [ToLD: Efficient Time Series Forecasting via Tokenized Truncated Latent Diffusion](https://neurips.cc/virtual/2026/poster/156116)。来源：S4-286, S5-286。通用时序候选（待全文确认）。
- [ ] ATT-0287 [ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2026/hash/65e17c5eb95d72ad6540e8f862f38ab1-Abstract-Conference.html)。来源：S4-287, S5-287。通用时序候选（待全文确认）。
- [ ] ATT-0288 [DiffPTS: Rethinking Diffusion ELBO for Probabilistic Time Series Forecasting](https://neurips.cc/virtual/2026/poster/148542)。来源：S4-288, S5-288。通用时序候选（待全文确认）。
- [ ] ATT-0289 [Koopman Generative Operators for Efficient Probabilistic Time-Series Forecasting](https://neurips.cc/virtual/2026/poster/153665)。来源：S4-289, S5-289。通用时序候选（待全文确认）。
- [ ] ATT-0290 [Probabilistic Circuits for Irregular Multivariate Time Series Forecasting](https://neurips.cc/virtual/2026/poster/152394)。来源：S4-290, S5-290。通用时序候选（待全文确认）。
- [ ] ATT-0291 [Beyond Extrapolation: Knowledge Utilization Paradigm with Bidirectional Inspiration for Time Series Forecasting](https://proceedings.mlr.press/v306/chong26c.html)。来源：S4-291, S5-291。通用时序候选（待全文确认）。
- [ ] ATT-0292 [Local Geometry Attention for Time Series Forecasting under Realistic Corruptions](https://proceedings.iclr.cc/paper_files/paper/2026/hash/476ab8f369e489c04187ba84f68cfa68-Abstract-Conference.html)。来源：S4-292, S5-292。通用时序候选（待全文确认）。
- [ ] ATT-0293 [TimeSeg: An Information-Theoretic Segment-Wise Explainer for Time-Series Predictions](https://proceedings.iclr.cc/paper_files/paper/2026/hash/f4fba41b554f9aaa013c4062a1c40518-Abstract-Conference.html)。来源：S4-293, S5-293。通用时序候选（待全文确认）。
- [ ] ATT-0294 [WILD: Widely Linear Conditioning for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/148890)。来源：S4-294, S5-294。通用时序候选（待全文确认）。
- [ ] ATT-0295 [CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Models](https://proceedings.mlr.press/v306/wang26ay.html)。来源：S4-295, S5-295。通用时序候选（待全文确认）。
- [ ] ATT-0296 [What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions](https://proceedings.mlr.press/v306/gu26r.html)。来源：S4-296, S5-296。通用时序候选（待全文确认）。
- [ ] ATT-0297 [DSR-TSF: Spectrum-Driven Dynamic Routing for Efficient Long-Horizon Time Series Forecasting](https://neurips.cc/virtual/2026/poster/152845)。来源：S4-297, S5-297。通用时序候选（待全文确认）。
- [ ] ATT-0298 [Unlocking the Value of Text: Event-Driven Reasoning and Multi-Level Alignment for Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2026/hash/34a1fc7890141f1ada3d8bc6199cce07-Abstract-Conference.html)。来源：S4-298, S5-298。通用时序候选（待全文确认）。
- [ ] ATT-0299 [SteerCast: Retrieval-Based Latent Steering for Decoder-Only Time Series Forecasting](https://neurips.cc/virtual/2026/poster/154457)。来源：S4-299, S5-299。通用时序候选（待全文确认）。
- [ ] ATT-0300 [Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models](https://proceedings.iclr.cc/paper_files/paper/2026/hash/d56a1a2b24b1906345fc2cd8276b11ed-Abstract-Conference.html)。来源：S4-300, S5-300。通用时序候选（待全文确认）。
- [ ] ATT-0301 [UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition](https://doi.org/10.1109/cvpr52733.2024.00527)。来源：S3-001。通用时序候选（待全文确认）。
- [ ] ATT-0302 [UrbanGPT: Spatio-Temporal Large Language Models](https://doi.org/10.1145/3637528.3671578)。来源：S3-002。领域专用；traffic_urban；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0303 [Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data](https://doi.org/10.1609/aaai.v38i14.29500)。来源：S3-003。时空（领域待核）。
- [ ] ATT-0304 [UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction](https://doi.org/10.1145/3637528.3671662)。来源：S3-004。领域专用；traffic_urban。
- [ ] ATT-0305 [Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective](https://doi.org/10.1145/3589334.3645710)。来源：S3-005。通用时序候选（待全文确认）。
- [ ] ATT-0306 [GPT4MTS: Prompt-based Large Language Model for Multimodal Time-series Forecasting](https://doi.org/10.1609/aaai.v38i21.30383)。来源：S3-006。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0307 [Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification](https://doi.org/10.52202/079017-1145)。来源：S3-007。领域专用；health_biomedicine。
- [ ] ATT-0308 [Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting](https://ijcai.org/proceedings/2024/442)。来源：S3-008。时空（领域待核）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0309 [Breaking the Time-Frequency Granularity Discrepancy in Time-Series Anomaly Detection](https://doi.org/10.1145/3589334.3645556)。来源：S3-009。通用时序候选（待全文确认）。
- [ ] ATT-0310 [A Unified Replay-Based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Data](https://doi.org/10.1109/icde60146.2024.00085)。来源：S3-010。时空（领域待核）。
- [ ] ATT-0311 [A Survey on Deep Learning based Time Series Analysis with Frequency Transformation](https://doi.org/10.1145/3711896.3736571)。来源：S3-011。通用时序候选（待全文确认）。
- [ ] ATT-0312 [FNSPID: A Comprehensive Financial News Dataset in Time Series](https://doi.org/10.1145/3637528.3671629)。来源：S3-012。领域专用；finance。
- [ ] ATT-0313 [Empowering Time Series Analysis with Large Language Models: A Survey](https://ijcai.org/proceedings/2024/895)。来源：S3-013。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0314 [SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting](https://doi.org/10.1145/3746252.3761394)。来源：S3-014。通用时序候选（待全文确认）。
- [ ] ATT-0315 [Earthfarsser: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model](https://doi.org/10.1609/aaai.v38i14.29521)。来源：S3-015。时空（领域待核）。
- [ ] ATT-0316 [Temporal-Frequency Masked Autoencoders for Time Series Anomaly Detection](https://doi.org/10.1109/icde60146.2024.00099)。来源：S3-016。通用时序候选（待全文确认）。
- [ ] ATT-0317 [Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis](https://doi.org/10.1145/3627673.3679614)。来源：S3-017。通用时序候选（待全文确认）。
- [ ] ATT-0318 [Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection](https://doi.org/10.1145/3711896.3737239)。来源：S3-018。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0319 [CUTS+: High-Dimensional Causal Discovery from Irregular Time-Series](https://doi.org/10.1609/aaai.v38i10.29034)。来源：S3-019。通用时序候选（待全文确认）。
- [ ] ATT-0320 [MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization](https://doi.org/10.1145/3616855.3635820)。来源：S3-020。领域专用；traffic_urban。
- [ ] ATT-0321 [PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection](https://doi.org/10.1145/3637528.3671753)。来源：S3-021。通用时序候选（待全文确认）。
- [ ] ATT-0322 [ShapeFormer: Shapelet Transformer for Multivariate Time Series Classification](https://doi.org/10.1145/3637528.3671862)。来源：S3-022。通用时序候选（待全文确认）。
- [ ] ATT-0323 [Deep Learning for Multivariate Time Series Imputation: A Survey](https://ijcai.org/proceedings/2025/1187)。来源：S3-023。通用时序候选（待全文确认）。
- [ ] ATT-0324 [When Model Meets New Normals: Test-Time Adaptation for Unsupervised Time-Series Anomaly Detection](https://doi.org/10.1609/aaai.v38i12.29210)。来源：S3-024。通用时序候选（待全文确认）。
- [ ] ATT-0325 [Multi-modal Time Series Analysis: A Tutorial and Survey](https://doi.org/10.1145/3711896.3736567)。来源：S3-025。通用时序候选（待全文确认）；与现榜近似题名待核。
- [ ] ATT-0326 [MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent Imputation](https://doi.org/10.1145/3627673.3679532)。来源：S3-026。通用时序候选（待全文确认）。
- [ ] ATT-0327 [Position: What Can Large Language Models Tell Us about Time Series Analysis](https://proceedings.mlr.press/v235/jin24i.html)。来源：S3-027。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0328 [Graph-Aware Contrasting for Multivariate Time-Series Classification](https://doi.org/10.1609/aaai.v38i14.29501)。来源：S3-028。通用时序候选（待全文确认）。
- [ ] ATT-0329 [Diffusion Language-Shapelets for Semi-supervised Time-Series Classification](https://doi.org/10.1609/aaai.v38i13.29317)。来源：S3-029。通用时序候选（待全文确认）。
- [ ] ATT-0330 [Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection](https://ijcai.org/proceedings/2024/239)。来源：S3-030。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0331 [Skip-Timeformer: Skip-Time Interaction Transformer for Long Sequence Time-Series Forecasting](https://ijcai.org/proceedings/2024/608)。来源：S3-031。通用时序候选（待全文确认）。
- [ ] ATT-0332 [CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking](https://doi.org/10.1145/3616855.3635795)。来源：S3-032。通用时序候选（待全文确认）。
- [ ] ATT-0333 [DiffDA: a Diffusion model for weather-scale Data Assimilation](https://proceedings.mlr.press/v235/huang24h.html)。来源：S3-033。领域专用；weather_climate。
- [ ] ATT-0334 [Advances in Time-Series Anomaly Detection: Algorithms, Benchmarks, and Evaluation Measures](https://doi.org/10.1145/3711896.3736565)。来源：S3-034。通用时序候选（待全文确认）。
- [ ] ATT-0335 [SensitiveHUE: Multivariate Time Series Anomaly Detection by Enhancing the Sensitivity to Normal Patterns](https://doi.org/10.1145/3637528.3671919)。来源：S3-035。通用时序候选（待全文确认）。
- [ ] ATT-0336 [Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detection](https://ojs.aaai.org/index.php/AAAI/article/view/33921)。来源：S3-036。通用时序候选（待全文确认）。
- [ ] ATT-0337 [TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection](https://doi.org/10.1145/3690624.3709266)。来源：S3-037。通用时序候选（待全文确认）。
- [ ] ATT-0338 [SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting](https://doi.org/10.1109/icde60146.2024.00101)。来源：S3-038。通用时序候选（待全文确认）。
- [ ] ATT-0339 [Unraveling the ‘Anomaly’ in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution](https://doi.org/10.1109/icde60146.2024.00080)。来源：S3-039。通用时序候选（待全文确认）。
- [ ] ATT-0340 [GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causality](https://ojs.aaai.org/index.php/AAAI/article/view/34096)。来源：S3-040。通用时序候选（待全文确认）。
- [ ] ATT-0341 [E2Usd: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series](https://doi.org/10.1145/3589334.3645593)。来源：S3-041。通用时序候选（待全文确认）。
- [ ] ATT-0342 [Learning Time-Aware Graph Structures for Spatially Correlated Time Series Forecasting](https://doi.org/10.1109/icde60146.2024.00338)。来源：S3-042。通用时序候选（待全文确认）。
- [ ] ATT-0343 [Multivariate Time Series Anomaly Detection by Capturing Coarse-Grained Intra- and Inter-Variate Dependencies](https://doi.org/10.1145/3696410.3714941)。来源：S3-043。通用时序候选（待全文确认）。
- [ ] ATT-0344 [An Interactive Dive into Time-Series Anomaly Detection](https://doi.org/10.1109/icde60146.2024.00409)。来源：S3-044。通用时序候选（待全文确认）。
- [ ] ATT-0345 [Conformal Prediction Regions for Time Series Using Linear Complementarity Programming](https://doi.org/10.1609/aaai.v38i19.30089)。来源：S3-045。通用时序候选（待全文确认）。
- [ ] ATT-0346 [MPTSNet: Integrating Multiscale Periodic Local Patterns and Global Dependencies for Multivariate Time Series Classification](https://ojs.aaai.org/index.php/AAAI/article/view/34155)。来源：S3-046。通用时序候选（待全文确认）。
- [ ] ATT-0347 [Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation](https://doi.org/10.1145/3627673.3679642)。来源：S3-047。时空（领域待核）。
- [ ] ATT-0348 [Soft Contrastive Learning for Time Series](https://proceedings.iclr.cc/paper_files/paper/2024/hash/ccc48eade8845cbc0b44384e8c49889a-Abstract-Conference.html)。来源：S3-048。通用时序候选（待全文确认）。
- [ ] ATT-0349 [CATCH: Channel-Aware Multivariate Time Series Anomaly Detection via Frequency Patching](https://proceedings.iclr.cc/paper_files/paper/2025/hash/2b25c39788e5cf11d3541de433ebf4c0-Abstract-Conference.html)。来源：S3-049。通用时序候选（待全文确认）。
- [ ] ATT-0350 [MSHTrans: Multi-Scale Hypergraph Transformer with Time-Series Decomposition for Temporal Anomaly Detection](https://doi.org/10.1145/3711896.3737057)。来源：S3-050。通用时序候选（待全文确认）。
- [ ] ATT-0351 [LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection](https://doi.org/10.1145/3589334.3645472)。来源：S3-051。通用时序候选（待全文确认）。
- [ ] ATT-0352 [Cross-Domain Pre-training with Language Models for Transferable Time Series Representations](https://doi.org/10.1145/3701551.3703498)。来源：S3-052。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0353 [SSD-TS: Exploring the potential of linear state space models for diffusion models in time series imputation](https://doi.org/10.1145/3711896.3737135)。来源：S3-053。通用时序候选（待全文确认）。
- [ ] ATT-0354 [Can Multimodal LLMs Perform Time Series Anomaly Detection?](https://doi.org/10.1145/3774904.3792376)。来源：S3-054。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0355 [Spatio-temporal Heterogeneous Federated Learning for Time Series Classification with Multi-view Orthogonal Training](https://doi.org/10.1145/3664647.3680733)。来源：S3-055。时空（领域待核）。
- [ ] ATT-0356 [Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data](https://proceedings.iclr.cc/paper_files/paper/2024/hash/a61023ce36d21010f1423304f8ec49af-Abstract-Conference.html)。来源：S3-056。通用时序候选（待全文确认）。
- [ ] ATT-0357 [TableTime: Reformulating Time Series Classification as Training-Free Table Understanding with Large Language Models](https://doi.org/10.1145/3746252.3761056)。来源：S3-057。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0358 [Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification](https://doi.org/10.1145/3696410.3714514)。来源：S3-058。领域专用；health_biomedicine。
- [ ] ATT-0359 [Frequency-aware Generative Models for Multivariate Time Series Imputation](https://proceedings.neurips.cc/paper_files/paper/2024/hash/5e364212327fa3a59ae3595b025c469f-Abstract-Conference.html)。来源：S3-059。通用时序候选（待全文确认）。
- [ ] ATT-0360 [FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification](https://doi.org/10.1145/3711896.3736969)。来源：S3-060。通用时序候选（待全文确认）。
- [ ] ATT-0361 [M3-Net: A Cost-Effective Graph-Free MLP-Based Model for Traffic Prediction](https://doi.org/10.1145/3746252.3760815)。来源：S3-061。领域专用；traffic_urban。
- [ ] ATT-0362 [POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning](https://doi.org/10.1145/3637528.3671721)。来源：S3-062。通用时序候选（待全文确认）。
- [ ] ATT-0363 [TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning](https://proceedings.mlr.press/v235/chen24af.html)。来源：S3-063。通用时序候选（待全文确认）。
- [ ] ATT-0364 [FinCast: A Foundation Model for Financial Time-Series Forecasting](https://doi.org/10.1145/3746252.3761261)。来源：S3-064。领域专用；finance；基础模型线索，须核复现门槛；与现榜近似题名待核。
- [ ] ATT-0365 [InstrucTime: Advancing Time Series Classification with Multimodal Language Modeling](https://doi.org/10.1145/3701551.3703499)。来源：S3-065。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0366 [Self-attention-based Diffusion Model for Time-series Imputation in Partial Blackout Scenarios](https://ojs.aaai.org/index.php/AAAI/article/view/33931)。来源：S3-066。通用时序候选（待全文确认）。
- [ ] ATT-0367 [TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillation](https://doi.org/10.1145/3770854.3780218)。来源：S3-067。通用时序候选（待全文确认）。
- [ ] ATT-0368 [Can LLMs Understand Time Series Anomalies?](https://proceedings.iclr.cc/paper_files/paper/2025/hash/05774fb74e863308c4b460c9f49f6918-Abstract-Conference.html)。来源：S3-068。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0369 [CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling](https://papers.nips.cc/paper_files/paper/2025/hash/c9658e8c20879632cb1cfca91d80ceb7-Abstract-Conference.html)。来源：S3-069。通用时序候选（待全文确认）。
- [ ] ATT-0370 [GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images](https://papers.nips.cc/paper_files/paper/2025/hash/8798321486948322be2b4d658744ba72-Abstract-Conference.html)。来源：S3-070。领域专用；health_biomedicine。
- [ ] ATT-0371 [SPACETIME: Causal Discovery from Non-Stationary Time Series](https://ojs.aaai.org/index.php/AAAI/article/view/34136)。来源：S3-071。通用时序候选（待全文确认）。
- [ ] ATT-0372 [AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data](https://doi.org/10.14778/3681954.3681978)。来源：S3-072。通用时序候选（待全文确认）。
- [ ] ATT-0373 [STONE: A Spatio-temporal OOD Learning Framework Kills Both Spatial and Temporal Shifts](https://doi.org/10.1145/3637528.3671680)。来源：S3-073。时空（领域待核）。
- [ ] ATT-0374 [Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement](https://aclanthology.org/2025.acl-long.1437/)。来源：S3-074。通用时序候选（待全文确认）。
- [ ] ATT-0375 [Context-Alignment: Activating and Enhancing LLMs Capabilities in Time Series](https://proceedings.iclr.cc/paper_files/paper/2025/hash/e1de63ec74f40d3234c4e053f3528e18-Abstract-Conference.html)。来源：S3-075。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0376 [Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative](https://proceedings.iclr.cc/paper_files/paper/2026/hash/29de5722d7d2a6946c08d0c0162a1c71-Abstract-Conference.html)。来源：S3-076。通用时序候选（待全文确认）。
- [ ] ATT-0377 [Mask the Redundancy: Evolving Masking Representation Learning for Multivariate Time-Series Clustering](https://ojs.aaai.org/index.php/AAAI/article/view/39777)。来源：S3-077。通用时序候选（待全文确认）。
- [ ] ATT-0378 [Content-aware Balanced Spectrum Encoding in Masked Modeling for Time Series Classification](https://ojs.aaai.org/index.php/AAAI/article/view/33875)。来源：S3-078。通用时序候选（待全文确认）。
- [ ] ATT-0379 [DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysis](https://ojs.aaai.org/index.php/AAAI/article/view/34173)。来源：S3-079。通用时序候选（待全文确认）。
- [ ] ATT-0380 [Understanding Why Large Language Models Can Be Ineffective in Time Series Analysis: The Impact of Modality Alignment](https://doi.org/10.1145/3711896.3737169)。来源：S3-080。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0381 [FreDN: Spectral Disentanglement for Time Series Forecasting via Learnable Frequency Decomposition](https://ojs.aaai.org/index.php/AAAI/article/view/39042)。来源：S3-081。通用时序候选（待全文确认）。
- [ ] ATT-0382 [Dataset Condensation for Time Series Classification via Dual Domain Matching](https://doi.org/10.1145/3637528.3671675)。来源：S3-082。通用时序候选（待全文确认）。
- [ ] ATT-0383 [Dynamic Multi-Network Mining of Tensor Time Series](https://doi.org/10.1145/3589334.3645461)。来源：S3-083。通用时序候选（待全文确认）。
- [ ] ATT-0384 [Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series](https://doi.org/10.1145/3637528.3671760)。来源：S3-084。通用时序候选（待全文确认）。
- [ ] ATT-0385 [Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach](https://proceedings.iclr.cc/paper_files/paper/2024/hash/2b04d138128b76c33e30ef73b771d0f4-Abstract-Conference.html)。来源：S3-085。通用时序候选（待全文确认）。
- [ ] ATT-0386 [EMAformer: Enhancing Transformer Through Embedding Armor for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/40095)。来源：S3-086。通用时序候选（待全文确认）。
- [ ] ATT-0387 [Towards Non-Stationary Time Series Forecasting with Temporal Stabilization and Frequency Differencing](https://ojs.aaai.org/index.php/AAAI/article/view/39585)。来源：S3-087。通用时序候选（待全文确认）。
- [ ] ATT-0388 [XLinear: A Lightweight and Accurate MLP-Based Model for Long-Term Time Series Forecasting with Exogenous Inputs](https://ojs.aaai.org/index.php/AAAI/article/view/39121)。来源：S3-088。通用时序候选（待全文确认）。
- [ ] ATT-0389 [BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition](https://proceedings.mlr.press/v235/fang24d.html)。来源：S3-089。通用时序候选（待全文确认）。
- [ ] ATT-0390 [Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data](https://doi.org/10.1145/3637528.3672023)。来源：S3-090。通用时序候选（待全文确认）。
- [ ] ATT-0391 [ReCTSi: Resource-efficient Correlated Time Series Imputation via Decoupled Pattern Learning and Completeness-aware Attentions](https://doi.org/10.1145/3637528.3671816)。来源：S3-091。通用时序候选（待全文确认）。
- [ ] ATT-0392 [Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels](https://doi.org/10.1145/3690624.3709257)。来源：S3-092。通用时序候选（待全文确认）。
- [ ] ATT-0393 [Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomalies](https://doi.org/10.1145/3711896.3737110)。来源：S3-093。通用时序候选（待全文确认）。
- [ ] ATT-0394 [TAIL-MIL: Time-Aware and Instance-Learnable Multiple Instance Learning for Multivariate Time Series Anomaly Detection](https://ojs.aaai.org/index.php/AAAI/article/view/33933)。来源：S3-094。通用时序候选（待全文确认）。
- [ ] ATT-0395 [DeepSenseMoE: Harnessing Power of Time Series Foundation Models for Few-Shot Human Activity Recognition](https://ojs.aaai.org/index.php/AAAI/article/view/36990)。来源：S3-095。领域专用；motion_activity_sensing；基础模型线索，须核复现门槛。
- [ ] ATT-0396 [Goal-Oriented Time-Series Forecasting: Foundation Framework Design](https://ojs.aaai.org/index.php/AAAI/article/view/39249)。来源：S3-096。通用时序候选（待全文确认）；基础模型线索，须核复现门槛。
- [ ] ATT-0397 [M2FMoE: Multi-Resolution Multi-View Frequency Mixture-of-Experts for Extreme-Adaptive Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/39362)。来源：S3-097。通用时序候选（待全文确认）。
- [ ] ATT-0398 [TSPulse: Tiny Pre-Trained Models with Disentangled Representations for Rapid Time-Series Analysis](https://proceedings.iclr.cc/paper_files/paper/2026/hash/6572721b15834cb37599abec23b5784f-Abstract-Conference.html)。来源：S3-098。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0399 [TimeX++: Learning Time-Series Explanations with Information Bottleneck](https://proceedings.mlr.press/v235/liu24bl.html)。来源：S3-099。通用时序候选（待全文确认）。
- [ ] ATT-0400 [BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecasting](https://doi.org/10.1145/3746252.3761278)。来源：S3-100。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0401 [Enhancing Masked Time-Series Modeling via Dropping Patches](https://ojs.aaai.org/index.php/AAAI/article/view/34211)。来源：S3-101。通用时序候选（待全文确认）。
- [ ] ATT-0402 [InfoMAE: Pair-Efficient Cross-Modal Alignment for Multimodal Time-Series Sensing Signals](https://doi.org/10.1145/3696410.3714853)。来源：S3-102。领域专用；motion_activity_sensing。
- [ ] ATT-0403 [Integrating Sequence and Image Modeling in Irregular Medical Time Series Through Self-Supervised Learning](https://ojs.aaai.org/index.php/AAAI/article/view/33737)。来源：S3-103。领域专用；health_biomedicine。
- [ ] ATT-0404 [ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Dataset](https://proceedings.mlr.press/v267/wang25av.html)。来源：S3-104。通用时序候选（待全文确认）。
- [ ] ATT-0405 [Motif-aware Graph Neural Networks for Networked Time Series Imputation](https://ojs.aaai.org/index.php/AAAI/article/view/33241)。来源：S3-105。时空（领域待核）。
- [ ] ATT-0406 [Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders](https://proceedings.iclr.cc/paper_files/paper/2025/hash/ca7998666c2e53cc1e882b7268414d8a-Abstract-Conference.html)。来源：S3-106。通用时序候选（待全文确认）。
- [ ] ATT-0407 [OccamVTS: Distilling Vision Models to 1% Parameters for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/39601)。来源：S3-107。通用时序候选（待全文确认）。
- [ ] ATT-0408 [S^2-KD: Semantic-Spectral Knowledge Distillation Spatiotemporal Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/37091)。来源：S3-108。时空（领域待核）。
- [ ] ATT-0409 [TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding](https://ojs.aaai.org/index.php/AAAI/article/view/39218)。来源：S3-109。通用时序候选（待全文确认）。
- [ ] ATT-0410 [Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification](https://proceedings.neurips.cc/paper_files/paper/2024/hash/a7c57c8942b7d2adba1ac615ba0e60b9-Abstract-Conference.html)。来源：S3-110。通用时序候选（待全文确认）。
- [ ] ATT-0411 [Efficient Discovery of Time Series Motifs under both Length Differences and Warping](https://doi.org/10.1145/3637528.3671726)。来源：S3-111。通用时序候选（待全文确认）。
- [ ] ATT-0412 [Knowledge-Empowered Dynamic Graph Network for Irregularly Sampled Medical Time Series](https://proceedings.neurips.cc/paper_files/paper/2024/hash/7c04aea54c2a60a632a47bd451cd2849-Abstract-Conference.html)。来源：S3-112。领域专用；health_biomedicine。
- [ ] ATT-0413 [Orthogonality Matters: Invariant Time Series Representation for Out-of-distribution Classification](https://doi.org/10.1145/3637528.3671768)。来源：S3-113。通用时序候选（待全文确认）。
- [ ] ATT-0414 [Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?](https://proceedings.mlr.press/v235/sarfraz24a.html)。来源：S3-114。通用时序候选（待全文确认）。
- [ ] ATT-0415 [EAPformer: Entropy-Aware Patch Transformer for Multivariate Long-Term Time Series Forecasting](https://doi.org/10.1145/3746252.3761055)。来源：S3-115。通用时序候选（待全文确认）。
- [ ] ATT-0416 [Learning Reliable and Intuitive Temporal Logic Rules for Interpretable Time Series Classification](https://doi.org/10.1145/3711896.3737022)。来源：S3-116。通用时序候选（待全文确认）。
- [ ] ATT-0417 [Mixture-of-KAN for Multivariate Time Series Forecasting](https://doi.org/10.1145/3746252.3760836)。来源：S3-117。通用时序候选（待全文确认）。
- [ ] ATT-0418 [TarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generation](https://doi.org/10.1145/3711896.3737147)。来源：S3-118。领域专用；health_biomedicine。
- [ ] ATT-0419 [TVNet: A Novel Time Series Analysis Method Based on Dynamic Convolution and 3D-Variation](https://proceedings.iclr.cc/paper_files/paper/2025/hash/58b9a640af6d69781e90969d936e87ce-Abstract-Conference.html)。来源：S3-119。通用时序候选（待全文确认）。
- [ ] ATT-0420 [Harnessing Vision-Language Models for Time Series Anomaly Detection](https://ojs.aaai.org/index.php/AAAI/article/view/39319)。来源：S3-120。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0421 [TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis](https://proceedings.mlr.press/v306/feng26s.html)。来源：S3-121。领域专用；traffic_urban。
- [ ] ATT-0422 [TemporalBench: A Benchmark for Evaluating LLM-Based Agents on Contextual and Event-Informed Time Series Tasks](https://doi.org/10.1145/3770855.3817485)。来源：S3-122。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0423 [Time series saliency maps: Explaining models across multiple domains](https://proceedings.mlr.press/v306/kechris26a.html)。来源：S3-123。通用时序候选（待全文确认）。
- [ ] ATT-0424 [Building Timeseries Dataset: Empowering Large-Scale Building Analytics](https://proceedings.neurips.cc/paper_files/paper/2024/hash/f0430903a14db90e5ce96f101902d6d7-Abstract-Datasets_and_Benchmarks_Track.html)。来源：S3-124。领域专用；industry_infrastructure。
- [ ] ATT-0425 [CAFO: Feature-Centric Explanation on Time Series Classification](https://doi.org/10.1145/3637528.3671724)。来源：S3-125。通用时序候选（待全文确认）。
- [ ] ATT-0426 [Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification](https://proceedings.neurips.cc/paper_files/paper/2024/hash/c6b96bf6380bd81bbfbce5db0e054a41-Abstract-Conference.html)。来源：S3-126。通用时序候选（待全文确认）。
- [ ] ATT-0427 [Discovering Mixtures of Structural Causal Models from Time Series Data](https://proceedings.mlr.press/v235/varambally24a.html)。来源：S3-127。通用时序候选（待全文确认）。
- [ ] ATT-0428 [Explaining Time Series via Contrastive and Locally Sparse Perturbations](https://proceedings.iclr.cc/paper_files/paper/2024/hash/bd9b0b6ffab928b68c2b126d50f26381-Abstract-Conference.html)。来源：S3-128。通用时序候选（待全文确认）。
- [ ] ATT-0429 [REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning](https://proceedings.iclr.cc/paper_files/paper/2024/hash/439bf902de1807088d8b731ca20b0777-Abstract-Conference.html)。来源：S3-129。通用时序候选（待全文确认）。
- [ ] ATT-0430 [Semi-Supervised Learning for Time Series Collected at a Low Sampling Rate](https://doi.org/10.1145/3637528.3672033)。来源：S3-130。通用时序候选（待全文确认）。
- [ ] ATT-0431 [Language Pre-training Guided Masking Representation Learning for Time Series Classification](https://ojs.aaai.org/index.php/AAAI/article/view/33377)。来源：S3-131。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0432 [Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data](https://proceedings.iclr.cc/paper_files/paper/2025/hash/d4c961804d08e55d898cce944206d455-Abstract-Conference.html)。来源：S3-132。通用时序候选（待全文确认）。
- [ ] ATT-0433 [Learning Soft Sparse Shapes for Efficient Time-Series Classification](https://proceedings.mlr.press/v267/liu25ar.html)。来源：S3-133。通用时序候选（待全文确认）。
- [ ] ATT-0434 [Real-E: A Foundation Benchmark for Advancing Robust and Generalizable Electricity Forecasting](https://doi.org/10.1145/3746252.3761637)。来源：S3-134。领域专用；energy_solar/traffic_urban。
- [ ] ATT-0435 [ST-LINK: Spatially-Aware Large Language Models for Spatio-Temporal Forecasting](https://doi.org/10.1145/3746252.3761085)。来源：S3-135。时空（领域待核）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0436 [TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation](https://proceedings.mlr.press/v267/jang25a.html)。来源：S3-136。通用时序候选（待全文确认）。
- [ ] ATT-0437 [A Unified Shape-Aware Foundation Model for Time Series Classification](https://ojs.aaai.org/index.php/AAAI/article/view/39574)。来源：S3-137。通用时序候选（待全文确认）；基础模型线索，须核复现门槛。
- [ ] ATT-0438 [EEO-TFV: Escape-Explore Optimizer for Web-Scale Time-Series Forecasting and Vision Analysis](https://doi.org/10.1145/3774904.3792434)。来源：S3-138。通用时序候选（待全文确认）。
- [ ] ATT-0439 [From Causal Discovery to Dynamic Causal Inference in Neural Time Series](https://doi.org/10.1145/3770855.3818956)。来源：S3-139。通用时序候选（待全文确认）。
- [ ] ATT-0440 [From Tokenizer Bias to Backbone Capability: A Controlled Study of LLMs for Time Series Forecasting](https://doi.org/10.1145/3770854.3780318)。来源：S3-140。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0441 [HyperD: Hybrid Periodicity Decoupling Framework for Traffic Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/38599)。来源：S3-141。领域专用；traffic_urban。
- [ ] ATT-0442 [MedSpaformer: A Transferable Transformer with Multi-Granularity Token Sparsification for Medical Time Series Classification](https://ojs.aaai.org/index.php/AAAI/article/view/40001)。来源：S3-142。领域专用；health_biomedicine。
- [ ] ATT-0443 [Optimal Look-back Horizon for Time Series Forecasting in Federated Learning](https://ojs.aaai.org/index.php/AAAI/article/view/39781)。来源：S3-143。通用时序候选（待全文确认）。
- [ ] ATT-0444 [T3Time: Tri-Modal Time Series Forecasting via Adaptive Multi-Head Alignment and Residual Fusion](https://ojs.aaai.org/index.php/AAAI/article/view/39196)。来源：S3-144。通用时序候选（待全文确认）。
- [ ] ATT-0445 [TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models](https://proceedings.mlr.press/v306/oublal26a.html)。来源：S3-145。通用时序候选（待全文确认）。
- [ ] ATT-0446 [AdaPatch: Adaptive Patch-Level Modeling for Non-Stationary Time Series Forecasting](https://doi.org/10.1145/3746252.3761360)。来源：S3-146。通用时序候选（待全文确认）。
- [ ] ATT-0447 [AutoDW-TS: Automated Data Wrangling for Time-Series Data](https://doi.org/10.1145/3746252.3761524)。来源：S3-147。通用时序候选（待全文确认）。
- [ ] ATT-0448 [Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detection](https://proceedings.mlr.press/v267/kim25aa.html)。来源：S3-148。通用时序候选（待全文确认）。
- [ ] ATT-0449 [Decoder-only Pre-training Enhancement for Spatio-temporal Traffic Forecasting](https://doi.org/10.1145/3746252.3761432)。来源：S3-149。领域专用；traffic_urban；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0450 [Enhancing Microservices Anomaly Detection via Multimodal Data Fusion in the Wavelet Domain and Spatiotemporal Graph-based Diffusion Probabilistic Model](https://doi.org/10.1145/3711896.3736938)。来源：S3-150。领域专用；industry_infrastructure。
- [ ] ATT-0451 [Enhancing Multivariate Time-Series Domain Adaptation via Contrastive Frequency Graph Discovery and Language-Guided Adversary Alignment](https://ojs.aaai.org/index.php/AAAI/article/view/33863)。来源：S3-151。通用时序候选（待全文确认）。
- [ ] ATT-0452 [Frequency-Conditioned Diffusion Models for Time Series Generation](https://doi.org/10.1145/3746252.3761050)。来源：S3-152。通用时序候选（待全文确认）；与现榜近似题名待核。
- [ ] ATT-0453 [Loss or Gain: Hierarchical Conditional Information Bottleneck Approach for Incomplete Time Series Classification](https://doi.org/10.1145/3711896.3737033)。来源：S3-153。通用时序候选（待全文确认）。
- [ ] ATT-0454 [Mixture of Online and Offline Experts for Non-Stationary Time Series](https://ojs.aaai.org/index.php/AAAI/article/view/34448)。来源：S3-154。通用时序候选（待全文确认）。
- [ ] ATT-0455 [MTM: A Multi-Scale Token Mixing Transformer for Irregular Multivariate Time Series Classification](https://doi.org/10.1145/3711896.3737058)。来源：S3-155。通用时序候选（待全文确认）。
- [ ] ATT-0456 [Neural Temporal Point Processes for Forecasting Directional Relations in Evolving Hypergraphs](https://ojs.aaai.org/index.php/AAAI/article/view/33856)。来源：S3-156。通用时序候选（待全文确认）。
- [ ] ATT-0457 [PromptTSS: A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentation](https://doi.org/10.1145/3746252.3761142)。来源：S3-157。通用时序候选（待全文确认）。
- [ ] ATT-0458 [Prospective Multi-Graph Cohesion for Multivariate Time Series Anomaly Detection](https://doi.org/10.1145/3701551.3703494)。来源：S3-158。通用时序候选（待全文确认）。
- [ ] ATT-0459 [Sampling-guided Heterogeneous Graph Neural Network with Temporal Smoothing for Scalable Longitudinal Data Imputation](https://doi.org/10.1145/3711896.3737116)。来源：S3-159。通用时序候选（待全文确认）。
- [ ] ATT-0460 [ScatterAD: Temporal-Topological Scattering Mechanism for Time Series Anomaly Detection](https://papers.nips.cc/paper_files/paper/2025/hash/4b52b3c50110fc10f6a1a86055682ea2-Abstract-Conference.html)。来源：S3-160。通用时序候选（待全文确认）。
- [ ] ATT-0461 [Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection](https://papers.nips.cc/paper_files/paper/2025/hash/5565ab682d6c7f8d9da34ba0919974b0-Abstract-Conference.html)。来源：S3-161。通用时序候选（待全文确认）。
- [ ] ATT-0462 [TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning](https://proceedings.mlr.press/v267/shapira-weber25a.html)。来源：S3-162。通用时序候选（待全文确认）。
- [ ] ATT-0463 [WDformer: A Wavelet-based Differential Transformer Model for Time Series Forecasting](https://doi.org/10.1145/3746252.3761348)。来源：S3-163。通用时序候选（待全文确认）。
- [ ] ATT-0464 [AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE](https://doi.org/10.1145/3770855.3817786)。来源：S3-164。通用时序候选（待全文确认）。
- [ ] ATT-0465 [CANDI: Curated Test-Time Adaptation for Multivariate Time-Series Anomaly Detection Under Distribution Shift](https://ojs.aaai.org/index.php/AAAI/article/view/38524)。来源：S3-165。通用时序候选（待全文确认）。
- [ ] ATT-0466 [Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series](https://proceedings.mlr.press/v306/murad26a.html)。来源：S3-166。通用时序候选（待全文确认）。
- [ ] ATT-0467 [DeepBooTS: Dual-Stream Residual Boosting for Drift-Resilient Time-Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/39509)。来源：S3-167。通用时序候选（待全文确认）。
- [ ] ATT-0468 [Dynamic Semantic Tokenization for Time Series via Elastic Sampling on Physics-aware Perception](https://ojs.aaai.org/index.php/AAAI/article/view/39517)。来源：S3-168。通用时序候选（待全文确认）。
- [ ] ATT-0469 [Enhancing Large Language Models for Time-Series Forecasting via Vector-Injected In-Context Learning](https://doi.org/10.1145/3774904.3792374)。来源：S3-169。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0470 [Finding Time Series Anomalies Using Granular-Ball Vector Data Description](https://ojs.aaai.org/index.php/AAAI/article/view/39722)。来源：S3-170。通用时序候选（待全文确认）。
- [ ] ATT-0471 [FreqCycle: A Multi-Scale Time-Frequency Analysis Method for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/40042)。来源：S3-171。通用时序候选（待全文确认）。
- [ ] ATT-0472 [Generalising Traffic Forecasting to Regions Without Traffic Observations](https://ojs.aaai.org/index.php/AAAI/article/view/38607)。来源：S3-172。领域专用；traffic_urban。
- [ ] ATT-0473 [Incident-Guided Spatiotemporal Traffic Forecasting](https://doi.org/10.1145/3770854.3780215)。来源：S3-173。领域专用；traffic_urban。
- [ ] ATT-0474 [Integrating Weather Foundation Model and Satellite to Enable Fine-Grained Solar Irradiance Forecasting](https://doi.org/10.1145/3770855.3818436)。来源：S3-174。领域专用；weather_climate/energy_solar；基础模型线索，须核复现门槛。
- [ ] ATT-0475 [Multi-Source Information Driven Spatio-Temporal Hypergraph Learning for Traffic Forecasting](https://doi.org/10.1145/3774904.3792119)。来源：S3-175。领域专用；traffic_urban。
- [ ] ATT-0476 [Position: A Dynamical Systems Perspective is Needed to Advance Time Series Modeling](https://proceedings.mlr.press/v306/durstewitz26a.html)。来源：S3-176。通用时序候选（待全文确认）。
- [ ] ATT-0477 [Role Hypergraph Contrastive Learning for Multivariate Time-Series Analysis](https://ojs.aaai.org/index.php/AAAI/article/view/39965)。来源：S3-177。通用时序候选（待全文确认）。
- [ ] ATT-0478 [Sonnet: Spectral Operator Neural Network for Multivariable Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/39736)。来源：S3-178。通用时序候选（待全文确认）。
- [ ] ATT-0479 [ST-VLM: A Spatial-to-Image Multimodal Spatial-Temporal Prediction Framework with Vision-Language Model](https://ojs.aaai.org/index.php/AAAI/article/view/38683)。来源：S3-179。时空（领域待核）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0480 [StarEmbed: Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars](https://proceedings.mlr.press/v306/li26t.html)。来源：S3-180。领域专用；astronomy；基础模型线索，须核复现门槛。
- [ ] ATT-0481 [State-Derivative-Aware Neural Controlled Differential Equations for Multivariate Time Series Anomaly Detection and Diagnosis](https://ojs.aaai.org/index.php/AAAI/article/view/39770)。来源：S3-181。通用时序候选（待全文确认）。
- [ ] ATT-0482 [Time Series Forecasting via Direct Per-Step Probability Distribution Modeling](https://ojs.aaai.org/index.php/AAAI/article/view/39426)。来源：S3-182。通用时序候选（待全文确认）。
- [ ] ATT-0483 [TimeBridge: Better Diffusion Prior Design with Bridge Models for Time Series Generation](https://doi.org/10.1145/3770854.3780195)。来源：S3-183。通用时序候选（待全文确认）。
- [ ] ATT-0484 [VisionST: Coordinating Cross-modal Traffic Prediction with Interactive Geo-image Encoding](https://doi.org/10.1145/3774904.3792447)。来源：S3-184。领域专用；traffic_urban。
- [ ] ATT-0485 [ViTs: Teaching Machines to See Time Series Anomalies Like Human Experts](https://doi.org/10.1145/3774904.3792323)。来源：S3-185。通用时序候选（待全文确认）。
- [ ] ATT-0486 [Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis](https://proceedings.mlr.press/v306/fu26n.html)。来源：S3-186。通用时序候选（待全文确认）；基础模型线索，须核复现门槛。
- [ ] ATT-0487 [SARAD: Spatial Association-Aware Anomaly Detection and Diagnosis for Multivariate Time Series](https://proceedings.neurips.cc/paper_files/paper/2024/hash/56ad264ac7448239145606cf4106042f-Abstract-Conference.html)。来源：S3-187。通用时序候选（待全文确认）。
- [ ] ATT-0488 [Bi-Modal Learning for Networked Time Series](https://doi.org/10.1145/3711896.3736856)。来源：S3-188。时空（领域待核）。
- [ ] ATT-0489 [Bidirectional Temporal-Aware Modeling with Multi-Scale Mixture-of-Experts for Multivariate Time Series Forecasting](https://doi.org/10.1145/3746252.3761273)。来源：S3-189。通用时序候选（待全文确认）。
- [ ] ATT-0490 [Causal Discovery from Conditionally Stationary Time Series](https://proceedings.mlr.press/v267/balsells-rodas25a.html)。来源：S3-190。通用时序候选（待全文确认）。
- [ ] ATT-0491 [Compositional simulation-based inference for time series](https://proceedings.iclr.cc/paper_files/paper/2025/hash/aa539a128dc8653408dab242f6042c84-Abstract-Conference.html)。来源：S3-191。通用时序候选（待全文确认）。
- [ ] ATT-0492 [DANet: A RAG-inspired Dual Attention Model for Few-shot Time Series Prediction](https://doi.org/10.1145/3746252.3761012)。来源：S3-192。通用时序候选（待全文确认）。
- [ ] ATT-0493 [Diffusion-Guided Diversity for Single Domain Generalization in Time Series Classification](https://doi.org/10.1145/3711896.3736909)。来源：S3-193。通用时序候选（待全文确认）。
- [ ] ATT-0494 [Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement](https://proceedings.iclr.cc/paper_files/paper/2025/hash/e1fcd183ab33714a8464e4e9a20ac710-Abstract-Conference.html)。来源：S3-194。通用时序候选（待全文确认）。
- [ ] ATT-0495 [Extracting Global Temporal Patterns Within Short Look-Back Windows for Traffic Forecasting](https://doi.org/10.1145/3746252.3761212)。来源：S3-195。领域专用；traffic_urban。
- [ ] ATT-0496 [FIC-TSC: Learning Time Series Classification with Fisher Information Constraint](https://proceedings.mlr.press/v267/chen25cq.html)。来源：S3-196。通用时序候选（待全文确认）。
- [ ] ATT-0497 [FRT: Flow-based Reconcile Transformer for Hierarchical Time Series](https://doi.org/10.1145/3711896.3737224)。来源：S3-197。通用时序候选（待全文确认）。
- [ ] ATT-0498 [Fully Quanvolutional Networks for Time Series Classification](https://doi.org/10.1145/3711896.3736972)。来源：S3-198。通用时序候选（待全文确认）。
- [ ] ATT-0499 [Generalized Discords for Time Series Anomaly Detection with Flexible Subsequence Lengths](https://doi.org/10.1145/3711896.3736977)。来源：S3-199。通用时序候选（待全文确认）。
- [ ] ATT-0500 [LAST SToP for Modeling Asynchronous Time Series](https://proceedings.mlr.press/v267/gupta25a.html)。来源：S3-200。通用时序候选（待全文确认）。
- [ ] ATT-0501 [LHMformer:Long-Range Historical Memory-Enhanced Transformer for Traffic Forecasting](https://doi.org/10.1145/3746252.3760966)。来源：S3-201。领域专用；traffic_urban。
- [ ] ATT-0502 [Mitigating Data Imbalance in Time Series Classification Based on Counterfactual Minority Samples Augmentation](https://doi.org/10.1145/3711896.3737049)。来源：S3-202。通用时序候选（待全文确认）。
- [ ] ATT-0503 [Mitigating Source Label Dependency in Time-Series Domain Adaptation under Label Shifts](https://doi.org/10.1145/3711896.3737050)。来源：S3-203。通用时序候选（待全文确认）。
- [ ] ATT-0504 [Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations](https://doi.org/10.1145/3746252.3760805)。来源：S3-204。领域专用；astronomy。
- [ ] ATT-0505 [Modeling Latent Non-Linear Dynamical System over Time Series](https://ojs.aaai.org/index.php/AAAI/article/view/33269)。来源：S3-205。通用时序候选（待全文确认）。
- [ ] ATT-0506 [Multivariate Wind Power Time Series Forecasting with Noise-Filtering Neural ODEs](https://doi.org/10.1145/3746252.3761118)。来源：S3-206。领域专用；energy_solar。
- [ ] ATT-0507 [ShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models](https://papers.nips.cc/paper_files/paper/2025/hash/26300d2734ce0c6bdac69b4c3187b5a2-Abstract-Conference.html)。来源：S3-207。通用时序候选（待全文确认）。
- [ ] ATT-0508 [Shifting the Paradigm: A Diffeomorphism Between Time Series Data Manifolds for Achieving Shift-Invariancy in Deep Learning](https://proceedings.iclr.cc/paper_files/paper/2025/hash/f631e778fd3c1b871e9e3a94369335e9-Abstract-Conference.html)。来源：S3-208。通用时序候选（待全文确认）。
- [ ] ATT-0509 [Solar Forecasting with Causality: A Graph-Transformer Approach to Spatiotemporal Dependencies](https://doi.org/10.1145/3746252.3760905)。来源：S3-209。领域专用；energy_solar。
- [ ] ATT-0510 [Spatio-Temporal Wavelet Enhanced Attention Mamba for Stock Price Forecasting](https://doi.org/10.1145/3746252.3761399)。来源：S3-210。领域专用；finance。
- [ ] ATT-0511 [Temporal Restoration and Spatial Rewiring for Source-Free Multivariate Time Series Domain Adaptation](https://doi.org/10.1145/3711896.3737150)。来源：S3-211。通用时序候选（待全文确认）。
- [ ] ATT-0512 [TopKNet:Learning to Perceive the Top-K Pivotal Nodes in Spatio-Temporal Data for Traffic Forecasting](https://doi.org/10.1145/3746252.3760993)。来源：S3-212。领域专用；traffic_urban。
- [ ] ATT-0513 [A Set-Sequence Model for Time Series](https://neurips.cc/virtual/2026/poster/148413)。来源：S3-213。通用时序候选（待全文确认）。
- [ ] ATT-0514 [Adaptive Frequency Pathways for Spatiotemporal Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/38595)。来源：S3-214。时空（领域待核）。
- [ ] ATT-0515 [AHA: Scalable Alternative History Analysis for Operational Timeseries Applications](https://doi.org/10.1145/3770854.3783945)。来源：S3-215。通用时序候选（待全文确认）。
- [ ] ATT-0516 [Beyond MSE: Ordinal Cross-Entropy for Probabilistic Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/39831)。来源：S3-216。通用时序候选（待全文确认）。
- [ ] ATT-0517 [Beyond Observations: Reconstruction Error-Guided Irregularly Sampled Time Series Representation Learning](https://ojs.aaai.org/index.php/AAAI/article/view/39545)。来源：S3-217。通用时序候选（待全文确认）。
- [ ] ATT-0518 [Contextual and Seasonal LSTMs for Time Series Anomaly Detection](https://proceedings.iclr.cc/paper_files/paper/2026/hash/761c3284ee4859bff3c7e5d9299a45ee-Abstract-Conference.html)。来源：S3-218。通用时序候选（待全文确认）。
- [ ] ATT-0519 [Counterfactual eXplainable AI (XAI) Method for Deep Learning-Based Multivariate Time Series Classification](https://ojs.aaai.org/index.php/AAAI/article/view/38792)。来源：S3-219。通用时序候选（待全文确认）。
- [ ] ATT-0520 [Cross-city Time Series Forecasting with Retrieval-Augmented Large Language Models](https://doi.org/10.1145/3774904.3792074)。来源：S3-220。领域专用；traffic_urban；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0521 [DiM-TS: Bridge the Gap Between Selective State Space Models and Time Series for Generative Modeling](https://ojs.aaai.org/index.php/AAAI/article/view/38649)。来源：S3-221。通用时序候选（待全文确认）。
- [ ] ATT-0522 [Dual Data-centric Separation with Circular Mixup for Noise-resistant Time Series Learning](https://doi.org/10.1145/3770854.3780265)。来源：S3-222。通用时序候选（待全文确认）。
- [ ] ATT-0523 [Dynamic Relational Priming Improves Transformer in Multivariate Time Series](https://proceedings.mlr.press/v306/lee26h.html)。来源：S3-223。通用时序候选（待全文确认）。
- [ ] ATT-0524 [EdgeMTSC: A Lightweight Large-Kernel ConvNet for Multivariate Time Series Classification](https://ojs.aaai.org/index.php/AAAI/article/view/38693)。来源：S3-224。通用时序候选（待全文确认）。
- [ ] ATT-0525 [EMFormer: Efficient Multi-Scale Transformer for Accumulative Context Weather Forecasting](https://proceedings.mlr.press/v306/chen26ao.html)。来源：S3-225。领域专用；weather_climate。
- [ ] ATT-0526 [Evolving Proxy Kills Drift: Data-Efficient Streaming Time Series Anomaly Detection](https://doi.org/10.1145/3774904.3792440)。来源：S3-226。通用时序候选（待全文确认）。
- [ ] ATT-0527 [FedRMamba: Federated Residual Mamba for Multivariate Time-Series Forecasting](https://doi.org/10.1145/3774904.3792712)。来源：S3-227。通用时序候选（待全文确认）。
- [ ] ATT-0528 [Giving Sensors a Voice: Multimodal JEPA for Semantic Time-Series Embeddings](https://proceedings.mlr.press/v306/dutta26b.html)。来源：S3-228。通用时序候选（待全文确认）。
- [ ] ATT-0529 [Harmonic Dataset Distillation for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/39328)。来源：S3-229。通用时序候选（待全文确认）。
- [ ] ATT-0530 [HEARTS: Benchmarking LLM Reasoning on Health Time Series](https://proceedings.mlr.press/v306/li26hr.html)。来源：S3-230。领域专用；health_biomedicine；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0531 [How to Train Your Mamba for Time Series Forecasting](https://doi.org/10.1145/3770854.3780279)。来源：S3-231。通用时序候选（待全文确认）。
- [ ] ATT-0532 [Koopman Invariants as Drivers of Emergent Time-Series Clustering in Joint-Embedding Predictive Architectures](https://ojs.aaai.org/index.php/AAAI/article/view/39708)。来源：S3-232。通用时序候选（待全文确认）。
- [ ] ATT-0533 [MDMLP-EIA: Multi-domain Dynamic MLPs with Energy Invariant Attention for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/40049)。来源：S3-233。通用时序候选（待全文确认）。
- [ ] ATT-0534 [Neural Predictive Control to Coordinate Discrete- and Continuous-Time Models for Time-Series Analysis with Control-Theoretical Improvements](https://doi.org/10.1145/3770854.3780261)。来源：S3-234。通用时序候选（待全文确认）。
- [ ] ATT-0535 [ORTCL: Towards Continual Learning of Time Series Foundation Models on Streaming Data via Orthogonal Rotation](https://ojs.aaai.org/index.php/AAAI/article/view/39526)。来源：S3-235。通用时序候选（待全文确认）；基础模型线索，须核复现门槛。
- [ ] ATT-0536 [Predictability-Aware Compression and Decompression Framework for Multichannel Time Series Data with Latent Seasonality](https://doi.org/10.1145/3774904.3792488)。来源：S3-236。通用时序候选（待全文确认）。
- [ ] ATT-0537 [Retrieval-Augmented Generation with Covariate Time Series](https://doi.org/10.1145/3770855.3818471)。来源：S3-237。通用时序候选（待全文确认）。
- [ ] ATT-0538 [Revitalizing Canonical Pre-Alignment for Irregular Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/40149)。来源：S3-238。通用时序候选（待全文确认）。
- [ ] ATT-0539 [SDFlow: Similarity-Driven Flow Matching for Time Series Generation](https://neurips.cc/virtual/2026/poster/150124)。来源：S3-239。通用时序候选（待全文确认）。
- [ ] ATT-0540 [Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases](https://proceedings.mlr.press/v306/tan26h.html)。来源：S3-240。通用时序候选（待全文确认）。
- [ ] ATT-0541 [Sparse-Scale Transformer with Bidirectional Awareness for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/39566)。来源：S3-241。通用时序候选（待全文确认）。
- [ ] ATT-0542 [SVGL: Scale-Variable Graph Learning in Model Space for Multivariate Time Series Classification](https://ojs.aaai.org/index.php/AAAI/article/view/39555)。来源：S3-242。通用时序候选（待全文确认）。
- [ ] ATT-0543 [Time Series, Vision, and Language: Exploring the Limits of Alignment in Contrastive Representation Spaces](https://proceedings.mlr.press/v306/yashwante26a.html)。来源：S3-243。通用时序候选（待全文确认）。
- [ ] ATT-0544 [TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning](https://neurips.cc/virtual/2026/poster/153295)。来源：S3-244。通用时序候选（待全文确认）。
- [ ] ATT-0545 [TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts](https://proceedings.mlr.press/v306/lin26al.html)。来源：S3-245。通用时序候选（待全文确认）。
- [ ] ATT-0546 [Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection](https://neurips.cc/virtual/2026/poster/151460)。来源：S3-246。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0547 [TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Models](https://proceedings.mlr.press/v306/yu26m.html)。来源：S3-247。通用时序候选（待全文确认）。
- [ ] ATT-0548 [Tyche: One Step Flow for Efficient Probabilistic Weather Forecasting](https://neurips.cc/virtual/2026/poster/153693)。来源：S3-248。领域专用；weather_climate。
- [ ] ATT-0549 [UniCA: Unified Covariate Adaptation for Time Series Foundation Model](https://proceedings.iclr.cc/paper_files/paper/2026/hash/0b5eb45a22ff33956c043dd271f244ea-Abstract-Conference.html)。来源：S3-249。通用时序候选（待全文确认）；基础模型线索，须核复现门槛。
- [ ] ATT-0550 [We Need a More Robust Classifier: Dual Causal Learning Empowers Domain-Incremental Time Series Classification](https://doi.org/10.1145/3774904.3792373)。来源：S3-250。通用时序候选（待全文确认）。
- [ ] ATT-0551 [EMIT - Event Based Masked Auto Encoding for Irregular Time Series](https://doi.org/10.1109/icdm59182.2024.00044)。来源：S3-251。通用时序候选（待全文确认）。
- [ ] ATT-0552 [Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark](https://aclanthology.org/2024.emnlp-main.1204/)。来源：S3-252。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0553 [Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder](https://doi.org/10.1109/icdm59182.2024.00041)。来源：S3-253。通用时序候选（待全文确认）。
- [ ] ATT-0554 [TS-CLIP: Time Series Understanding by CLIP](https://aclanthology.org/2025.emnlp-main.231/)。来源：S3-254。通用时序候选（待全文确认）。
- [ ] ATT-0555 [STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning](https://aclanthology.org/2026.acl-long.702/)。来源：S3-255。时空（领域待核）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0556 [CrisisTS: Coupling Social Media Textual Data and Meteorological Time Series for Urgency Classification](https://aclanthology.org/2025.acl-long.783/)。来源：S3-256。领域专用；weather_climate。
- [ ] ATT-0557 [LETS-C: Leveraging Text Embedding for Time Series Classification](https://aclanthology.org/2025.acl-long.1557/)。来源：S3-257。通用时序候选（待全文确认）。
- [ ] ATT-0558 [TSVer: A Benchmark for Fact Verification Against Time-Series Evidence](https://aclanthology.org/2025.emnlp-main.1519/)。来源：S3-258。通用时序候选（待全文确认）。
- [ ] ATT-0559 [Markovian Linguistic-Temporal Bridge: Unlocking the Potential of LLMs for Time Series Forecasting](https://aclanthology.org/2026.acl-long.1014/)。来源：S3-259。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0560 [ZARA: Training-Free Motion Time-Series Reasoning via Evidence-Grounded LLM Agents](https://aclanthology.org/2026.acl-long.684/)。来源：S3-260。领域专用；motion_activity_sensing；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0561 [An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series](https://proceedings.mlr.press/v235/huang24r.html)。来源：S3-261。通用时序候选（待全文确认）。
- [ ] ATT-0562 [An Online Bootstrap for Time Series](https://proceedings.mlr.press/v238/palm24a.html)。来源：S3-262。通用时序候选（待全文确认）。
- [ ] ATT-0563 [An Unsupervised Approach for Periodic Source Detection in Time Series](https://proceedings.mlr.press/v235/demirel24b.html)。来源：S3-263。通用时序候选（待全文确认）。
- [ ] ATT-0564 [Better Batch for Deep Probabilistic Time Series Forecasting](https://proceedings.mlr.press/v238/zheng24a.html)。来源：S3-264。通用时序候选（待全文确认）。
- [ ] ATT-0565 [CauDiTS: Causal Disentangled Domain Adaptation of Multivariate Time Series](https://proceedings.mlr.press/v235/lu24i.html)。来源：S3-265。通用时序候选（待全文确认）。
- [ ] ATT-0566 [Conditional Information Bottleneck Approach for Time Series Imputation](https://proceedings.iclr.cc/paper_files/paper/2024/hash/de7858e3e7f9f0f7b2c7bfdc86f6d928-Abstract-Conference.html)。来源：S3-266。通用时序候选（待全文确认）。
- [ ] ATT-0567 [Cross-Domain Contrastive Learning for Time Series Clustering](https://ojs.aaai.org/index.php/AAAI/article/view/28740)。来源：S3-267。通用时序候选（待全文确认）。
- [ ] ATT-0568 [Cumulative Difference Learning VAE for Time-Series with Temporally Correlated Inflow-Outflow](https://ojs.aaai.org/index.php/AAAI/article/view/29266)。来源：S3-268。通用时序候选（待全文确认）。
- [ ] ATT-0569 [Energy Efficient Streaming Time Series Classification with Attentive Power Iteration](https://ojs.aaai.org/index.php/AAAI/article/view/29151)。来源：S3-269。通用时序候选（待全文确认）。
- [ ] ATT-0570 [Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks](https://proceedings.mlr.press/v238/september24a.html)。来源：S3-270。通用时序候选（待全文确认）。
- [ ] ATT-0571 [Fitting ARMA Time Series Models without Identification: A Proximal Approach](https://proceedings.mlr.press/v238/liu24e.html)。来源：S3-271。通用时序候选（待全文确认）。
- [ ] ATT-0572 [GAFormer: Enhancing Timeseries Transformers Through Group-Aware Embeddings](https://proceedings.iclr.cc/paper_files/paper/2024/hash/2d4eaf042567f1c03c086103cc154c1f-Abstract-Conference.html)。来源：S3-272。通用时序候选（待全文确认）。
- [ ] ATT-0573 [Guaranteeing Robustness Against Real-World Perturbations In Time Series Classification Using Conformalized Randomized Smoothing](https://proceedings.mlr.press/v244/franco24a.html)。来源：S3-273。通用时序候选（待全文确认）。
- [ ] ATT-0574 [Identifiability of total effects from abstractions of time series causal graphs](https://proceedings.mlr.press/v244/assaad24a.html)。来源：S3-274。通用时序候选（待全文确认）。
- [ ] ATT-0575 [ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation](https://doi.org/10.1145/3637528.3671751)。来源：S3-275。时空（领域待核）。
- [ ] ATT-0576 [Inherently Interpretable Time Series Classification via Multiple Instance Learning](https://proceedings.iclr.cc/paper_files/paper/2024/hash/fb9d01fb202e360b2f78510c47e0fa3e-Abstract-Conference.html)。来源：S3-276。通用时序候选（待全文确认）。
- [ ] ATT-0577 [IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers](https://ojs.aaai.org/index.php/AAAI/article/view/29534)。来源：S3-277。领域专用；health_biomedicine。
- [ ] ATT-0578 [Leveraging Generative Models for Unsupervised Alignment of Neural Time Series Data](https://proceedings.iclr.cc/paper_files/paper/2024/hash/93fab021315170101c92e8330a56fbdb-Abstract-Conference.html)。来源：S3-278。通用时序候选（待全文确认）。
- [ ] ATT-0579 [Mixture-of-Linear-Experts for Long-term Time Series Forecasting](https://proceedings.mlr.press/v238/ni24a.html)。来源：S3-279。通用时序候选（待全文确认）。
- [ ] ATT-0580 [Multivariate Time Series Forecasting By Graph Attention Networks With Theoretical Guarantees](https://proceedings.mlr.press/v238/zhang24g.html)。来源：S3-280。通用时序候选（待全文确认）。
- [ ] ATT-0581 [Random Oscillators Network for Time Series Processing](https://proceedings.mlr.press/v238/ceni24a.html)。来源：S3-281。通用时序候选（待全文确认）。
- [ ] ATT-0582 [SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation](https://ojs.aaai.org/index.php/AAAI/article/view/29405)。来源：S3-282。通用时序候选（待全文确认）。
- [ ] ATT-0583 [SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series](https://proceedings.iclr.cc/paper_files/paper/2024/hash/5f7367f5686e6e1592e64209d172b91d-Abstract-Conference.html)。来源：S3-283。通用时序候选（待全文确认）。
- [ ] ATT-0584 [Towards the Disappearing Truth: Fine-Grained Joint Causal Influences Learning with Hidden Variable-Driven Causal Hypergraphs in Time Series](https://ojs.aaai.org/index.php/AAAI/article/view/29662)。来源：S3-284。通用时序候选（待全文确认）。
- [ ] ATT-0585 [Unsupervised Change Point Detection in Multivariate Time Series](https://proceedings.mlr.press/v238/wu24g.html)。来源：S3-285。通用时序候选（待全文确认）。
- [ ] ATT-0586 [When are dynamical systems learned from time series data statistically accurate?](https://proceedings.neurips.cc/paper_files/paper/2024/hash/4dc57702c987e1e72f0dd2921edb5ded-Abstract-Conference.html)。来源：S3-286。通用时序候选（待全文确认）。
- [ ] ATT-0587 [Adaptive Time Encoding for Irregular Multivariate Time-Series Classification](https://papers.nips.cc/paper_files/paper/2025/hash/934c0d630a07b3a088e273efcc5731c5-Abstract-Conference.html)。来源：S3-287。通用时序候选（待全文确认）。
- [ ] ATT-0588 [Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting](https://proceedings.mlr.press/v258/liu25l.html)。来源：S3-288。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0589 [Arrow: Accelerator for Time Series Causal Discovery with Time Weaving](https://proceedings.mlr.press/v267/yao25a.html)。来源：S3-289。通用时序候选（待全文确认）。
- [ ] ATT-0590 [Breakthrough Sensor-Limited Single View: Towards Implicit Temporal Dynamics for Time Series Domain Adaptation](https://papers.nips.cc/paper_files/paper/2025/hash/862f45ccecb2275851bc8acebb8b4d65-Abstract-Conference.html)。来源：S3-290。通用时序候选（待全文确认）。
- [ ] ATT-0591 [Bridging Time and Linguistics: LLMs as Time Series Analyzer through Symbolization and Segmentation](https://papers.nips.cc/paper_files/paper/2025/hash/b47c58a2c3949d298c93fff531178ba4-Abstract-Conference.html)。来源：S3-291。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0592 [Causal Discovery-Driven Change Point Detection in Time Series](https://proceedings.mlr.press/v258/gao25g.html)。来源：S3-292。通用时序候选（待全文确认）。
- [ ] ATT-0593 [Channel Matters: Estimating Channel Influence for Multivariate Time Series](https://papers.nips.cc/paper_files/paper/2025/hash/12df08e9280061b4877f97598c644c3e-Abstract-Conference.html)。来源：S3-293。通用时序候选（待全文确认）。
- [ ] ATT-0594 [ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables](https://proceedings.mlr.press/v258/arango25a.html)。来源：S3-294。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。
- [ ] ATT-0595 [Classification of High-dimensional Time Series in Spectral Domain Using Explainable Features with Applications to Neuroimaging Data](https://proceedings.mlr.press/v258/roy25a.html)。来源：S3-295。领域专用；health_biomedicine。
- [ ] ATT-0596 [Complete Characterization for Adjustment in Summary Causal Graphs of Time Series](https://proceedings.mlr.press/v286/yvernes25a.html)。来源：S3-296。通用时序候选（待全文确认）。
- [ ] ATT-0597 [Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models](https://proceedings.mlr.press/v286/ho25a.html)。来源：S3-297。时空（领域待核）。
- [ ] ATT-0598 [Contimask: Explaining Irregular Time Series via Perturbations in Continuous Time](https://papers.nips.cc/paper_files/paper/2025/hash/4eb5daabc45b45a9a312aa2c8fca8a74-Abstract-Conference.html)。来源：S3-298。通用时序候选（待全文确认）。
- [ ] ATT-0599 [DAAC: Discrepancy-Aware Adaptive Contrastive Learning for Medical Time series](https://papers.nips.cc/paper_files/paper/2025/hash/f36a180277bd3d5781dc02245f9d5f52-Abstract-Conference.html)。来源：S3-299。领域专用；health_biomedicine。
- [ ] ATT-0600 [Delving into Large Language Models for Effective Time-Series Anomaly Detection](https://papers.nips.cc/paper_files/paper/2025/hash/950a34c7b107111570077078e1b5b8ef-Abstract-Conference.html)。来源：S3-300。通用时序候选（待全文确认）；预训练/大模型依赖线索，须核资源。