# 2024—2026 年时间序列顶会新增候选：300 篇

筛选日期：2026-11-05。每条题名链接指向会议官网、官方论文集或 ACM DOI；列表用于下一轮阅读和审查，尚未对全部论文进行全文精读、独立复现或真实性评分。

## 比对基线与范围

- 原有清单取自你的私有仓库 [time-series-paper-ranking 的 inventory.json](https://github.com/GuangDai/time-series-paper-ranking/blob/4a11d69eb714c8934b57c13986a651533b3aea38/inventory.json)，固定提交 `4a11d69eb714c8934b57c13986a651533b3aea38`，共 **360** 条。本地快照的 Git blob 为 `ff1e76dff52de6349bdd9a69762c8dd9884922c0`，SHA-256 为 `eb4dc3c9df0eeffdccd3e60d9b284ef5cec5b856603fb59b7fe3159f5e054a68`。
- 网站当时显示 **345** 篇纳入排序；仓库首轮审计的 345 篇均包含在 360 条库存内。在线完整 345 项未能逐一导出，因此本表的“新增”准确含义是**未匹配到固定版本的 360 条库存**，与网站实时清单的逐项完全一致性仍待核对。
- 年份按正式会议年份计：2024 年 60 篇、2025 年 100 篇、2026 年 140 篇。配额用于覆盖三年，不表示各年的论文总量或学术水平比例。主要包括 ICLR、ICML、NeurIPS、KDD、AAAI、WWW；WWW 的具体论文类别仍需逐篇核实。
- 研究范围以时间序列预测为主，延伸到基础模型、时空与天气、校准和 conformal、分布漂移、基准评测、缺失/不规则序列等。表中“方向”是按标题进行的粗分类；分类或因果论文标有对应方向，不当作预测论文。
- 2026 年 NeurIPS 论文取自[官方录用和会议日程](https://neurips.cc/Downloads/2026)，其会议定于 2026 年 12 月举行；截至筛选日，这些条目标为**已录用，会议尚未召开**。其他会议的论文类型与赛道按可取得的官方元数据标注。
- 对题名进行大小写、标点、Unicode 和部分 LaTeX 规范化，与库存做完整题名和 URL 对照；相似题名达到 0.97 时保守排除。跨候选再按题名去重。改名版本、作者别名和 DOI 错链仍可能造成漏判，详见[去重记录](候选数据/去重记录.csv)。
- 独立复核了 300 条题名与库存及候选内部的近似匹配；69 条 `doi.org` 来源均通过 Crossref 元数据核对题名，52 条 NeurIPS 2026 来源均与官网录用状态和赛道核对。其余来源以各会议官方索引或论文页为依据，未对全部论文做全文审查。
- 本表是按主题和会议优先级做的候选筛选，不是会议论文的穷尽清单，也不是“300 篇都比已有论文更好”的结论。同分项使用固定哈希打散，避免仅按英文字母截断。

## 建议优先阅读

以下条目与现有项目的基准公平性、强依赖覆盖和天气预测复现最直接相关。说明依据题名与公开摘要，属于阅读线索。

- **#66 [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)**：数据集选择如何影响模型比较；对应 NoChampions 的基准偏差。
- **#198 [Beyond Model Ranking: Predictability-Aligned Evaluation for Time Series Forecasting](https://proceedings.mlr.press/v306/feng26u.html)**：按数据可预测性理解排名；对应模型比较的评价口径。
- **#214 [Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecasting](https://proceedings.mlr.press/v306/moretti26a.html)**：专门讨论时间序列基准问题；观点论文，需单独与实证论文区分。
- **#1 [RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms](https://doi.org/10.1145/3637528.3672062)**：层次时间序列预测的评估框架，适合检查任务和聚合规则。
- **#2 [Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift](https://doi.org/10.1145/3637528.3671926)**：时序预测校准与分布变化，适合分析模型失效条件。
- **#65 [Neural Conformal Control for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/34029)**：在线时序预测的 conformal 方法，可与现有覆盖率审计对照。
- **#161 [Dynamic Dual-Feedback Conformal Inference for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/155680)**：NeurIPS 2026 已录用，研究时序预测的 conformal 校准。
- **#170 [Evaluating Uncertainty Calibration in Probabilistic Time Series Foundation Models](https://neurips.cc/virtual/2026/poster/138974)**：NeurIPS 2026 评测赛道，评估时序基础模型的不确定性校准。
- **#162 [Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters](https://neurips.cc/virtual/2026/poster/154722)**：NeurIPS 2026 已录用，检验合成数据对预测模型的作用。
- **#173 [Why Do Time Series Models Need Long Context Windows?](https://neurips.cc/virtual/2026/poster/149819)**：NeurIPS 2026 已录用，适合检查长输入窗口的收益与成本。
- **#172 [Revisiting the Generic Transformer: Deconstructing a Strong Baseline for Time Series Foundation Models](https://neurips.cc/virtual/2026/poster/155883)**：NeurIPS 2026 已录用，从通用 Transformer 基线审视时序基础模型。
- **#40 [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)**：异常检测基准的可靠性，适合检查评价指标与数据集。
- **#124 [Fixing the Double Penalty in Data-Driven Weather Forecasting Through a Modified Spherical Harmonic Loss Function](https://proceedings.mlr.press/v267/subich25a.html)**：天气预测中的评分与损失设计，可与 GenCast 检查互补。
- **#171 [PhyTS: A Benchmark for Scientific Time Series](https://neurips.cc/virtual/2026/poster/139305)**：NeurIPS 2026 评测与数据集赛道，覆盖科学时序基准。

## 库存中发现的两处标题—来源错配

- 库存 **#125 Fredformer** 的 `primary_source_url` 指向一篇 NeurIPS 2023 的 FreTS 论文；Fredformer 的正式会议来源为 [KDD 2024 论文清单](https://kdd.org/kdd2024/research-track-papers/)及 [DOI: 10.1145/3637528.3671928](https://doi.org/10.1145/3637528.3671928)。
- 库存 **#307 Amplifier** 的来源 URL 指向上述 Fredformer DOI；Amplifier 的正式来源为 [AAAI 2025 官方论文页](https://ojs.aaai.org/index.php/AAAI/article/view/33267)。本次仅记录问题，未修改原仓库。

## 会议分布

| 年份 | ICLR | ICML | NeurIPS | KDD | AAAI | WWW | 合计 |
|---:|---:|---:|---:|---:|---:|---:|---:|
| 2024 | 8 | 9 | 17 | 18 | 7 | 1 | 60 |
| 2025 | 11 | 10 | 11 | 31 | 34 | 3 | 100 |
| 2026 | 20 | 45 | 52 | 15 | 7 | 1 | 140 |

## 300 篇候选论文

编号是索引，并非学术质量排名。NeurIPS 2026 条目均为官方已录用记录；逐篇论文类别或赛道按官方可核实字段标注。


### 2024 年

| 编号 | 论文与官方来源 | 会议与赛道 | 方向 |
|---:|---|---|---|
| 1 | [RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms](https://doi.org/10.1145/3637528.3672062) | KDD · 研究 | 评测、稳健性与不确定性 |
| 2 | [Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift](https://doi.org/10.1145/3637528.3671926) | KDD · 研究 | 评测、稳健性与不确定性 |
| 3 | [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) | NEURIPS · 数据集与基准 | 评测、稳健性与不确定性 |
| 4 | [Multi-Patch Prediction: Adapting Language Models for Time Series Representation Learning](https://proceedings.mlr.press/v235/bian24a.html) | ICML | 基础模型与时序表征 |
| 5 | [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) | NEURIPS · 主会 | 时空与场景预测 |
| 6 | [FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting](https://doi.org/10.1145/3637528.3671509) | KDD · 应用数据科学 | 时空与场景预测 |
| 7 | [Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/27768) | AAAI · 技术论文 | 时间序列预测 |
| 8 | [Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask](https://doi.org/10.1145/3637528.3671673) | KDD · 研究 | 缺失与不规则时序 |
| 9 | [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) | ICLR · 主会 | 缺失与不规则时序 |
| 10 | [STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction](https://proceedings.iclr.cc/paper_files/paper/2024/hash/832b20b65f655587e9c0447860406a82-Abstract-Conference.html) | ICLR · 主会 | 时间序列预测 |
| 11 | [Reservoir Computing for Short High-Dimensional Time Series: an Application to SARS-CoV-2 Hospitalization Forecast](https://proceedings.mlr.press/v235/ferte24a.html) | ICML | 时间序列预测 |
| 12 | [Efficient and Effective Time-Series Forecasting with Spiking Neural Networks](https://proceedings.mlr.press/v235/lv24d.html) | ICML | 时间序列预测 |
| 13 | [Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization](https://proceedings.mlr.press/v235/liu24aw.html) | ICML | 时间序列预测 |
| 14 | [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) | ICLR · 主会 | 时间序列预测 |
| 15 | [Towards Transparent Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2024/hash/b5414313268287725e91a8eae9ddea01-Abstract-Conference.html) | ICLR · 主会 | 时间序列预测 |
| 16 | [GraFITi: Graphs for Forecasting Irregularly Sampled Time Series](https://ojs.aaai.org/index.php/AAAI/article/view/29560) | AAAI · 技术论文 | 缺失与不规则时序 |
| 17 | [Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting](https://doi.org/10.1145/3637528.3671961) | KDD · 研究 | 时间序列预测 |
| 18 | [Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/29483) | AAAI · 技术论文 | 时间序列预测 |
| 19 | [MARLP: Time-series Forecasting Control for Agricultural Managed Aquifer Recharge](https://doi.org/10.1145/3637528.3671533) | KDD · 应用数据科学 | 时间序列预测 |
| 20 | [FRNet: Frequency-based Rotation Network for Long-term Time Series Forecasting](https://doi.org/10.1145/3637528.3671713) | KDD · 研究 | 时间序列预测 |
| 21 | [Explainable and Interpretable Forecasts on Non-Smooth Multivariate Time Series for Responsible Gameplay](https://doi.org/10.1145/3637528.3671657) | KDD · 应用数据科学 | 时间序列预测 |
| 22 | [CGS-Mask: Making Time Series Predictions Intuitive for All](https://ojs.aaai.org/index.php/AAAI/article/view/29325) | AAAI · 技术论文 | 时间序列预测 |
| 23 | [GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing](https://doi.org/10.1145/3637528.3672055) | KDD · 研究 | 缺失与不规则时序 |
| 24 | [Generative Pretrained Hierarchical Transformer for Time Series Forecasting](https://doi.org/10.1145/3637528.3671855) | KDD · 研究 | 时间序列预测 |
| 25 | [Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Networks](https://doi.org/10.1145/3637528.3671665) | KDD · 研究 | 缺失与不规则时序 |
| 26 | [AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting](https://doi.org/10.1145/3637528.3672057) | KDD · 研究 | 时间序列预测 |
| 27 | [Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization](https://doi.org/10.1145/3637528.3671969) | KDD · 研究 | 时间序列预测 |
| 28 | [Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity](https://doi.org/10.1145/3637528.3671632) | KDD · 应用数据科学 | 时间序列预测 |
| 29 | [Quantifying and Estimating the Predictability Upper Bound of Univariate Numeric Time Series](https://doi.org/10.1145/3637528.3671995) | KDD · 研究 | 时间序列预测 |
| 30 | [Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling](https://proceedings.mlr.press/v235/marisca24a.html) | ICML | 缺失与不规则时序 |
| 31 | [Towards a Self-contained Data-driven Global Weather Forecasting Framework](https://proceedings.mlr.press/v235/xiao24a.html) | ICML | 时空与场景预测 |
| 32 | [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) | AAAI · 技术论文 | 时空与场景预测 |
| 33 | [Spatio-Temporal Pivotal Graph Neural Networks for Traffic Flow Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/28707) | AAAI · 技术论文 | 时空与场景预测 |
| 34 | [Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations](https://doi.org/10.1145/3589334.3645688) | WWW · 论文类型待核 | 时空与场景预测 |
| 35 | [STEMO: Early Spatio-temporal Forecasting with Multi-Objective Reinforcement Learning](https://doi.org/10.1145/3637528.3671922) | KDD · 研究 | 时间序列预测 |
| 36 | [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) | NEURIPS · 数据集与基准 | 评测、稳健性与不确定性 |
| 37 | [Class-incremental Learning for Time Series: Benchmark and Evaluation](https://doi.org/10.1145/3637528.3671581) | KDD · 应用数据科学 | 评测、稳健性与不确定性 |
| 38 | [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) | ICLR · 主会 | 基础模型与时序表征 |
| 39 | [T-Rep: Representation Learning for Time Series using Time-Embeddings](https://proceedings.iclr.cc/paper_files/paper/2024/hash/84b946c4c4162b29464fc7aa57e480e1-Abstract-Conference.html) | ICLR · 主会 | 基础模型与时序表征 |
| 40 | [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) | NEURIPS · 数据集与基准 | 评测、稳健性与不确定性 |
| 41 | [Learning diverse causally emergent representations from time series data](https://proceedings.neurips.cc/paper_files/paper/2024/hash/d8398f4da88975e2a9c62ecaa5ba267b-Abstract-Conference.html) | NEURIPS · 主会 | 基础模型与时序表征 |
| 42 | [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) | NEURIPS · 主会 | 基础模型与时序表征 |
| 43 | [Exploiting Representation Curvature for Boundary Detection in Time Series](https://proceedings.neurips.cc/paper_files/paper/2024/hash/0b7f639ef28a9035a71f7e0c04c1d681-Abstract-Conference.html) | NEURIPS · 主会 | 基础模型与时序表征 |
| 44 | [UniMTS: Unified Pre-training for Motion Time Series](https://proceedings.neurips.cc/paper_files/paper/2024/hash/c290d4373c495b2cad0625d6288260f0-Abstract-Conference.html) | NEURIPS · 主会 | 基础模型与时序表征 |
| 45 | [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) | ICLR · 主会 | 基础模型与时序表征 |
| 46 | [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) | NEURIPS · 主会 | 基础模型与时序表征 |
| 47 | [Task-oriented Time Series Imputation Evaluation via Generalized Representers](https://proceedings.neurips.cc/paper_files/paper/2024/hash/f88264fcc54775ee1706116e90fe351a-Abstract-Conference.html) | NEURIPS · 主会 | 评测、稳健性与不确定性 |
| 48 | [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) | NEURIPS · 主会 | 基础模型与时序表征 |
| 49 | [MF-CLR: Multi-Frequency Contrastive Learning Representation for Time Series](https://proceedings.mlr.press/v235/duan24b.html) | ICML | 基础模型与时序表征 |
| 50 | [TimesURL: Self-Supervised Contrastive Learning for Universal Time Series Representation Learning](https://ojs.aaai.org/index.php/AAAI/article/view/29299) | AAAI · 技术论文 | 基础模型与时序表征 |
| 51 | [PATE: Proximity-Aware Time Series Anomaly Evaluation](https://doi.org/10.1145/3637528.3671971) | KDD · 研究 | 评测、稳健性与不确定性 |
| 52 | [Vector Quantization Pretraining for EEG Time Series with Random Projection and Phase Alignment](https://proceedings.mlr.press/v235/gui24a.html) | ICML | 时序建模与分析 |
| 53 | [ChronoEpilogi: Scalable Time Series Selection with Multiple Solutions](https://proceedings.neurips.cc/paper_files/paper/2024/hash/f24e8cc1c1c06a689850ee766a7357b2-Abstract-Conference.html) | NEURIPS · 主会 | 时序建模与分析 |
| 54 | [Reinforced Cross-Domain Knowledge Distillation on Time Series Data](https://proceedings.neurips.cc/paper_files/paper/2024/hash/5424f6b74f475ad738b54888d609283a-Abstract-Conference.html) | NEURIPS · 主会 | 时序建模与分析 |
| 55 | [Parametric Augmentation for Time Series Contrastive Learning](https://proceedings.iclr.cc/paper_files/paper/2024/hash/ccf6d8b4a1fe9d9c8192f00c713872ea-Abstract-Conference.html) | ICLR · 主会 | 时序建模与分析 |
| 56 | [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) | NEURIPS · 主会 | 时序建模与分析 |
| 57 | [Learning Causal Relations from Subsampled Time Series with Two Time-Slices](https://proceedings.mlr.press/v235/wu24p.html) | ICML | 时序因果分析 |
| 58 | [Rough Transformers: Lightweight and Continuous Time Series Modelling through Signature Patching](https://proceedings.neurips.cc/paper_files/paper/2024/hash/bfe167fee3be2d862a56af82dee77720-Abstract-Conference.html) | NEURIPS · 主会 | 时序建模与分析 |
| 59 | [Boosting Transferability and Discriminability for Time Series Domain Adaptation](https://proceedings.neurips.cc/paper_files/paper/2024/hash/b61da4f02b271cb7b5e3d538e2b78fb9-Abstract-Conference.html) | NEURIPS · 主会 | 时序建模与分析 |
| 60 | [Shape analysis for time series](https://proceedings.neurips.cc/paper_files/paper/2024/hash/ad86418f7bdfa685cd089e028efd75cd-Abstract-Conference.html) | NEURIPS · 主会 | 时序建模与分析 |

### 2025 年

| 编号 | 论文与官方来源 | 会议与赛道 | 方向 |
|---:|---|---|---|
| 61 | [Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates](https://doi.org/10.1145/3711896.3737046) | KDD · 研究 | 缺失与不规则时序 |
| 62 | [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) | ICLR · 主会 | 评测、稳健性与不确定性 |
| 63 | [Shifting Time: Time-series Forecasting with Khatri-Rao Neural Operators](https://proceedings.mlr.press/v267/dama25a.html) | ICML | 时间序列预测 |
| 64 | [CSformer: Combining Channel Independence and Mixing for Robust Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/35406) | AAAI · 技术论文 | 时间序列预测 |
| 65 | [Neural Conformal Control for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/34029) | AAAI · 技术论文 | 评测、稳健性与不确定性 |
| 66 | [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) | AAAI · 技术论文 | 评测、稳健性与不确定性 |
| 67 | [Learning with Calibration: Exploring Test-Time Computing of Spatio-Temporal Forecasting](https://papers.neurips.cc/paper_files/paper/2025/hash/e4a04155055153f18b93824825cd6e3d-Abstract-Conference.html) | NEURIPS · 主会 | 评测、稳健性与不确定性 |
| 68 | [Information Bottleneck-guided MLPs for Robust Spatial-temporal Forecasting](https://proceedings.mlr.press/v267/chen25ax.html) | ICML | 时间序列预测 |
| 69 | [FSTLLM: Spatio-Temporal LLM for Few Shot Time Series Forecasting](https://proceedings.mlr.press/v267/jiang25a.html) | ICML | 基础模型与时序表征 |
| 70 | [TimeCapsule: Solving the Jigsaw Puzzle of Long-Term Time Series Forecasting with Compressed Predictive Representations](https://doi.org/10.1145/3711896.3737157) | KDD · 研究 | 时间序列预测 |
| 71 | [When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series](https://proceedings.mlr.press/v267/park25e.html) | ICML | 异常检测 |
| 72 | [Investigating Pattern Neurons in Urban Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2025/hash/1fb38e9c776b5ff18511a284570b15c8-Abstract-Conference.html) | ICLR · 主会 | 时间序列预测 |
| 73 | [Locally Connected Echo State Networks for Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2025/hash/d8bd445c2abe1343cce0e14b361b2fb3-Abstract-Conference.html) | ICLR · 主会 | 时间序列预测 |
| 74 | [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) | ICLR · 主会 | 时间序列预测 |
| 75 | [LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization](https://proceedings.mlr.press/v267/niu25e.html) | ICML | 时间序列预测 |
| 76 | [Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting](https://proceedings.mlr.press/v267/schuchardt25a.html) | ICML | 时间序列预测 |
| 77 | [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) | ICLR · 主会 | 时间序列预测 |
| 78 | [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) | ICLR · 主会 | 时间序列预测 |
| 79 | [A Lightweight Sparse Interaction Network for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/33452) | AAAI · 技术论文 | 时间序列预测 |
| 80 | [Disentangling Long-Short Term State Under Unknown Interventions for Online Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/33717) | AAAI · 技术论文 | 时间序列预测 |
| 81 | [Probabilistic Hypergraph Recurrent Neural Networks for Time-series Forecasting](https://doi.org/10.1145/3690624.3709202) | KDD · 研究 | 时间序列预测 |
| 82 | [CMA: A Unified Contextual Meta-Adaptation Methodology for Time-Series Denoising and Prediction](https://doi.org/10.1145/3711896.3736881) | KDD · 研究 | 时间序列预测 |
| 83 | [VarDrop: Enhancing Training Efficiency by Reducing Variate Redundancy in Periodic Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/33951) | AAAI · 技术论文 | 时间序列预测 |
| 84 | [Times2D: Multi-Period Decomposition and Derivative Mapping for General Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/34164) | AAAI · 技术论文 | 时间序列预测 |
| 85 | [Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learning](https://doi.org/10.1145/3711896.3737174) | KDD · 研究 | 异常检测 |
| 86 | [CrossLinear: Plug-and-Play Cross-Correlation Embedding for Time Series Forecasting with Exogenous Variables](https://doi.org/10.1145/3711896.3736899) | KDD · 研究 | 时间序列预测 |
| 87 | [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) | AAAI · 技术论文 | 基础模型与时序表征 |
| 88 | [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) | AAAI · 技术论文 | 基础模型与时序表征 |
| 89 | [Quantum Time-index Models with Reservoir for Time Series Forecasting](https://doi.org/10.1145/3690624.3709228) | KDD · 研究 | 时间序列预测 |
| 90 | [Probabilistic Forecasting of Irregularly Sampled Time Series with Missing Values via Conditional Normalizing Flows](https://ojs.aaai.org/index.php/AAAI/article/view/35494) | AAAI · 技术论文 | 缺失与不规则时序 |
| 91 | [Test-Time Training with Diversified Local Aggregation Consistency for Mortality Prediction using Clinical Time Series](https://doi.org/10.1145/3711896.3737151) | KDD · 研究 | 时间序列预测 |
| 92 | [Proactive Model Adaptation Against Concept Drift for Online Time Series Forecasting](https://doi.org/10.1145/3690624.3709210) | KDD · 研究 | 时间序列预测 |
| 93 | [Sequence Complementor: Complementing Transformers for Time Series Forecasting with Learnable Sequences](https://ojs.aaai.org/index.php/AAAI/article/view/33747) | AAAI · 技术论文 | 时间序列预测 |
| 94 | [Timing is important: Risk-aware Fund Allocation based on Time-Series Forecasting](https://doi.org/10.1145/3711896.3737268) | KDD · 应用数据科学 | 时间序列预测 |
| 95 | [Unlocking the Power of LSTM for Long Term Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/33303) | AAAI · 技术论文 | 时间序列预测 |
| 96 | [ST-MTM: Masked Time Series Modeling with Seasonal-Trend Decomposition for Time Series Forecasting](https://doi.org/10.1145/3690624.3709254) | KDD · 研究 | 时间序列预测 |
| 97 | [Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/33908) | AAAI · 技术论文 | 时间序列预测 |
| 98 | [Hierarchical Classification Auxiliary Network for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/34286) | AAAI · 技术论文 | 时间序列预测 |
| 99 | [Semantics-Aware Patch Encoding and Hierarchical Dependency Modeling for Long-Term Time Series Forecasting](https://doi.org/10.1145/3711896.3737123) | KDD · 研究 | 时间序列预测 |
| 100 | [xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decomposition](https://ojs.aaai.org/index.php/AAAI/article/view/34270) | AAAI · 技术论文 | 时间序列预测 |
| 101 | [SDE: A Simplified and Disentangled Dependency Encoding Framework for State Space Models in Time Series Forecasting](https://doi.org/10.1145/3711896.3737119) | KDD · 研究 | 时间序列预测 |
| 102 | [FilterTS: Comprehensive Frequency Filtering for Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/35438) | AAAI · 技术论文 | 时间序列预测 |
| 103 | [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) | AAAI · 技术论文 | 时间序列预测 |
| 104 | [IN-Flow: Instance Normalization Flow for Non-stationary Time Series Forecasting](https://doi.org/10.1145/3690624.3709260) | KDD · 研究 | 时间序列预测 |
| 105 | [HyperMixer: Specializable Hypergraph Channel Mixing for Long-term Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/34302) | AAAI · 技术论文 | 时间序列预测 |
| 106 | [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) | KDD · 研究 | 时间序列预测 |
| 107 | [Beyond Fixed Variables: Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning](https://doi.org/10.1145/3711896.3736854) | KDD · 研究 | 时间序列预测 |
| 108 | [Revisiting Attention for Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/35455) | AAAI · 技术论文 | 时间序列预测 |
| 109 | [Stochastic Diffusion: A Diffusion Based Model for Stochastic Time Series Forecasting](https://doi.org/10.1145/3711896.3737137) | KDD · 研究 | 时间序列预测 |
| 110 | [HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/32057) | AAAI · 技术论文 | 时间序列预测 |
| 111 | [Multi-period Learning for Financial Time Series Forecasting](https://doi.org/10.1145/3690624.3709422) | KDD · 应用数据科学 | 时间序列预测 |
| 112 | [Exploiting Language Power for Time Series Forecasting with Exogenous Variables](https://doi.org/10.1145/3696410.3714793) | WWW · 论文类型待核 | 时间序列预测 |
| 113 | [Utilizing Strategic Pre-training to Reduce Overfitting: Baguan – A Pre-trained Weather Forecasting Model](https://doi.org/10.1145/3711896.3737178) | KDD · 研究 | 时空与场景预测 |
| 114 | [ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/33465) | AAAI · 技术论文 | 时间序列预测 |
| 115 | [Continuous Ensemble Weather Forecasting with Diffusion models](https://proceedings.iclr.cc/paper_files/paper/2025/hash/424403b4d2553f16d8ad11635a8214b3-Abstract-Conference.html) | ICLR · 主会 | 时空与场景预测 |
| 116 | [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) | NEURIPS · 主会 | 缺失与不规则时序 |
| 117 | [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) | NEURIPS · 主会 | 时空与场景预测 |
| 118 | [Decision-aware Training of Spatiotemporal Forecasting Models to Select a Top-K Subset of Sites for Intervention](https://proceedings.mlr.press/v267/heuton25a.html) | ICML | 时间序列预测 |
| 119 | [HR-Extreme: A High-Resolution Dataset for Extreme Weather Forecasting](https://proceedings.iclr.cc/paper_files/paper/2025/hash/3236f661fc6f00e8b16ff42db99acaaf-Abstract-Conference.html) | ICLR · 主会 | 时空与场景预测 |
| 120 | [TCP-Diffusion: A Multi-modal Diffusion Model for Global Tropical Cyclone Precipitation Forecasting with Change Awareness](https://proceedings.mlr.press/v267/huang25y.html) | ICML | 时空与场景预测 |
| 121 | [LoRA-EnVar: Parameter-Efficient Hybrid Ensemble Variational Assimilation for Weather Forecasting](https://papers.nips.cc/paper_files/paper/2025/hash/413885e70482b95dcbeeddc1daf39177-Abstract-Conference.html) | NEURIPS · 主会 | 时空与场景预测 |
| 122 | [FlowMixer: A Depth-Agnostic Neural Architecture for Interpretable Spatiotemporal Forecasting](https://papers.neurips.cc/paper_files/paper/2025/hash/803485352e61e3ebf41221e4776c9fd4-Abstract-Conference.html) | NEURIPS · 主会 | 时间序列预测 |
| 123 | [Less but More: Linear Adaptive Graph Learning Empowering Spatiotemporal Forecasting](https://papers.neurips.cc/paper_files/paper/2025/hash/54c9bfb0885ae07f23607f617ab64c2b-Abstract-Conference.html) | NEURIPS · 主会 | 时间序列预测 |
| 124 | [Fixing the Double Penalty in Data-Driven Weather Forecasting Through a Modified Spherical Harmonic Loss Function](https://proceedings.mlr.press/v267/subich25a.html) | ICML | 时空与场景预测 |
| 125 | [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) | NEURIPS · 主会 | 时空与场景预测 |
| 126 | [RadarQA: Multi-modal Quality Analysis of Weather Radar Forecasts](https://papers.nips.cc/paper_files/paper/2025/hash/beb667eee2fecb594dc3154950ef7aa4-Abstract-Conference.html) | NEURIPS · 主会 | 时空与场景预测 |
| 127 | [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) | ICLR · 主会 | 时间序列预测 |
| 128 | [STKOpt: Automated Spatio-Temporal Knowledge Optimization for Traffic Prediction](https://doi.org/10.1145/3696410.3714598) | WWW · 论文类型待核 | 时空与场景预测 |
| 129 | [Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting](https://doi.org/10.1145/3711896.3737081) | KDD · 研究 | 时空与场景预测 |
| 130 | [NRFormer: Nationwide Nuclear Radiation Forecasting with Spatio-Temporal Transformer](https://doi.org/10.1145/3711896.3737252) | KDD · 应用数据科学 | 时间序列预测 |
| 131 | [AutoSTF: Decoupled Neural Architecture Search for Cost-Effective Automated Spatio-Temporal Forecasting](https://doi.org/10.1145/3690624.3709323) | KDD · 研究 | 时间序列预测 |
| 132 | [ASTNet: Asynchronous Spatio-Temporal Network for Large-Scale Chemical Sensor Forecasting](https://doi.org/10.1145/3711896.3737194) | KDD · 应用数据科学 | 时间序列预测 |
| 133 | [Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective](https://doi.org/10.1145/3690624.3709177) | KDD · 研究 | 时空与场景预测 |
| 134 | [Decomposed Spatio-Temporal Mamba for Long-Term Traffic Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/33281) | AAAI · 技术论文 | 时空与场景预测 |
| 135 | [Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach](https://ojs.aaai.org/index.php/AAAI/article/view/33328) | AAAI · 技术论文 | 时间序列预测 |
| 136 | [EWMoE: An Effective Model for Global Weather Forecasting with Mixture-of-Experts](https://ojs.aaai.org/index.php/AAAI/article/view/31997) | AAAI · 技术论文 | 时空与场景预测 |
| 137 | [ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data](https://ojs.aaai.org/index.php/AAAI/article/view/33310) | AAAI · 技术论文 | 时间序列预测 |
| 138 | [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) | AAAI · 技术论文 | 时间序列预测 |
| 139 | [Spatial-Temporal Heterogenous Graph Contrastive Learning for Microservice Workload Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/33271) | AAAI · 技术论文 | 时间序列预测 |
| 140 | [DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting](https://doi.org/10.1145/3690624.3709391) | KDD · 应用数据科学 | 时间序列预测 |
| 141 | [Does Vector Quantization Fail in Spatio-Temporal Forecasting? Exploring a Differentiable Sparse Soft-Vector Quantization Approach](https://doi.org/10.1145/3711896.3736914) | KDD · 研究 | 时间序列预测 |
| 142 | [Efficient Traffic Prediction Through Spatio-Temporal Distillation](https://ojs.aaai.org/index.php/AAAI/article/view/32096) | AAAI · 技术论文 | 时空与场景预测 |
| 143 | [FairTP: A Prolonged Fairness Framework for Traffic Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/34838) | AAAI · 技术论文 | 时空与场景预测 |
| 144 | [UoMo: A Universal Model of Mobile Traffic Forecasting for Wireless Network Optimization](https://doi.org/10.1145/3711896.3737272) | KDD · 应用数据科学 | 时空与场景预测 |
| 145 | [SSL-STMFormer Self-Supervised Learning Spatio-Temporal Entanglement Transformer for Traffic Flow Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/33321) | AAAI · 技术论文 | 时空与场景预测 |
| 146 | [Toward Interpretable Evaluation Measures for Time Series Segmentation](https://papers.nips.cc/paper_files/paper/2025/hash/09425891e393e64b0535194a81ba15b7-Abstract-Conference.html) | NEURIPS · 主会 | 评测、稳健性与不确定性 |
| 147 | [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) | ICLR · 主会 | 评测、稳健性与不确定性 |
| 148 | [Real-Time Calibration Model for Low-Cost Sensor in Fine-Grained Time Series](https://ojs.aaai.org/index.php/AAAI/article/view/31974) | AAAI · 技术论文 | 评测、稳健性与不确定性 |
| 149 | [Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation](https://doi.org/10.1145/3690624.3709239) | KDD · 研究 | 评测、稳健性与不确定性 |
| 150 | [Learning Without Augmenting: Unsupervised Time Series Representation Learning via Frame Projections](https://papers.nips.cc/paper_files/paper/2025/hash/b05616c9f202380cd3075ddabb6bf86d-Abstract-Conference.html) | NEURIPS · 主会 | 基础模型与时序表征 |
| 151 | [AdaTS: Learning Adaptive Time Series Representations via Dynamic Soft Contrasts](https://papers.nips.cc/paper_files/paper/2025/hash/6c66efb65c3031bd09b2201ca9a28a74-Abstract-Conference.html) | NEURIPS · 主会 | 基础模型与时序表征 |
| 152 | [Time Series Representations with Hard-Coded Invariances](https://proceedings.mlr.press/v267/germain25a.html) | ICML | 基础模型与时序表征 |
| 153 | [Unleashing The Power of Pre-Trained Language Models for Irregularly Sampled Time Series](https://doi.org/10.1145/3711896.3737171) | KDD · 研究 | 缺失与不规则时序 |
| 154 | [FAT: Frequency-Aware Pretraining for Enhanced Time-Series Representation Learning](https://doi.org/10.1145/3711896.3736952) | KDD · 研究 | 基础模型与时序表征 |
| 155 | [Pre-training Time Series Models with Stock Data Customization](https://doi.org/10.1145/3711896.3737005) | KDD · 研究 | 基础模型与时序表征 |
| 156 | [Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals](https://doi.org/10.1145/3696410.3714931) | WWW · 论文类型待核 | 基础模型与时序表征 |
| 157 | [Federated Foundation Models on Heterogeneous Time Series](https://ojs.aaai.org/index.php/AAAI/article/view/33739) | AAAI · 技术论文 | 基础模型与时序表征 |
| 158 | [Frequency-Masked Embedding Inference: A Non-Contrastive Approach for Time Series Representation Learning](https://ojs.aaai.org/index.php/AAAI/article/view/33828) | AAAI · 技术论文 | 基础模型与时序表征 |
| 159 | [STEM-LTS: Integrating Semantic-Temporal Dynamics in LLM-driven Time Series Analysis](https://ojs.aaai.org/index.php/AAAI/article/view/34447) | AAAI · 技术论文 | 基础模型与时序表征 |
| 160 | [DyCAST: Learning Dynamic Causal Structure from Time Series](https://proceedings.iclr.cc/paper_files/paper/2025/hash/3918dd28e51ce20c38c8a1c05758e4a0-Abstract-Conference.html) | ICLR · 主会 | 时序因果分析 |

### 2026 年

| 编号 | 论文与官方来源 | 会议与赛道 | 方向 |
|---:|---|---|---|
| 161 | [Dynamic Dual-Feedback Conformal Inference for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/155680) | NEURIPS · 主会 | 评测、稳健性与不确定性 |
| 162 | [Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters](https://neurips.cc/virtual/2026/poster/154722) | NEURIPS · 主会 | 时间序列预测 |
| 163 | [GlucoFM-Bench: Benchmarking Time-Series Foundation Models for Blood Glucose Forecasting](https://neurips.cc/virtual/2026/poster/139533) | NEURIPS · 数据集与基准 | 评测、稳健性与不确定性 |
| 164 | [Differencing the Diffusion Trajectory toward Uncertain Components for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/154330) | NEURIPS · 主会 | 评测、稳健性与不确定性 |
| 165 | [Nuwa: Evaluation-Grounded Agentic Construction of Time Series Forecasting Systems](https://neurips.cc/virtual/2026/poster/153330) | NEURIPS · 主会 | 评测、稳健性与不确定性 |
| 166 | [FISC: Time-Series Forecasting via First-Layer Statistical Calibration Constraints](https://neurips.cc/virtual/2026/poster/149568) | NEURIPS · 主会 | 评测、稳健性与不确定性 |
| 167 | [Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach](https://neurips.cc/virtual/2026/poster/149487) | NEURIPS · 主会 | 时间序列预测 |
| 168 | [Robust Residual Correction via Selective Deployment for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/154644) | NEURIPS · 主会 | 时间序列预测 |
| 169 | [PAMod: Modeling Cyclical Shifts via Phase-Amplitude Modulation for Non-stationary Time Series Forecasting](https://neurips.cc/virtual/2026/poster/155815) | NEURIPS · 主会 | 时间序列预测 |
| 170 | [Evaluating Uncertainty Calibration in Probabilistic Time Series Foundation Models](https://neurips.cc/virtual/2026/poster/138974) | NEURIPS · 数据集与基准 | 评测、稳健性与不确定性 |
| 171 | [PhyTS: A Benchmark for Scientific Time Series](https://neurips.cc/virtual/2026/poster/139305) | NEURIPS · 数据集与基准 | 评测、稳健性与不确定性 |
| 172 | [Revisiting the Generic Transformer: Deconstructing a Strong Baseline for Time Series Foundation Models](https://neurips.cc/virtual/2026/poster/155883) | NEURIPS · 主会 | 基础模型与时序表征 |
| 173 | [Why Do Time Series Models Need Long Context Windows?](https://neurips.cc/virtual/2026/poster/149819) | NEURIPS · 主会 | 时序建模与分析 |
| 174 | [Filter Banks: from Low-Rank Representations to Deep Models for Efficient Time Series Forecasting](https://neurips.cc/virtual/2026/poster/152048) | NEURIPS · 主会 | 时间序列预测 |
| 175 | [When to Trust Memory: Retrieval-Guided Probabilistic Spatiotemporal Forecasting under Distribution Shift](https://neurips.cc/virtual/2026/poster/151106) | NEURIPS · 主会 | 评测、稳健性与不确定性 |
| 176 | [OrangeTree: A Linear and Tree-based Time Series Forecasting Model Supporting Multiple Input and Output Lengths](https://neurips.cc/virtual/2026/poster/151255) | NEURIPS · 主会 | 时间序列预测 |
| 177 | [CVTA: Cross-Variable Temporal Attention for Multivariate Irregular Time Series Prediction](https://neurips.cc/virtual/2026/poster/152025) | NEURIPS · 主会 | 缺失与不规则时序 |
| 178 | [TimeES: Probabilistic and Deterministic Time Series Forecasting via Evolutionary Spectra](https://neurips.cc/virtual/2026/poster/154967) | NEURIPS · 主会 | 时间序列预测 |
| 179 | [BucpTSF: Breaking the Uniform Computation Paradigm in Time-Series Forecasting](https://neurips.cc/virtual/2026/poster/148238) | NEURIPS · 主会 | 时间序列预测 |
| 180 | [Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting](https://neurips.cc/virtual/2026/poster/149780) | NEURIPS · 主会 | 时间序列预测 |
| 181 | [OTformer: Non-Stationarity-Aware Adaptive Optimal Transport Attention for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/148743) | NEURIPS · 主会 | 时间序列预测 |
| 182 | [HAMSTAR: Hamiltonian Structured Inter-Period Refinement for Long-Term Time Series Forecasting](https://neurips.cc/virtual/2026/poster/150931) | NEURIPS · 主会 | 时间序列预测 |
| 183 | [Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting](https://neurips.cc/virtual/2026/poster/154424) | NEURIPS · 主会 | 时间序列预测 |
| 184 | [TStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting](https://neurips.cc/virtual/2026/poster/149443) | NEURIPS · 主会 | 时间序列预测 |
| 185 | [Predictive Surprise as Self-Grounding Concept Bottleneck for Interpretable Time Series](https://neurips.cc/virtual/2026/poster/153183) | NEURIPS · 主会 | 时间序列预测 |
| 186 | [Beyond Isolation: Neighbor-Consistent Data Pruning for Multivariate Time Series Forecasting](https://neurips.cc/virtual/2026/poster/154771) | NEURIPS · 主会 | 时间序列预测 |
| 187 | [Paloma: Phase-Conditioned Residual Modulation for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/154087) | NEURIPS · 主会 | 时间序列预测 |
| 188 | [FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting](https://neurips.cc/virtual/2026/poster/151862) | NEURIPS · 主会 | 时间序列预测 |
| 189 | [QWaveNet: Quantum-Enhanced Wavelet Network for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/152829) | NEURIPS · 主会 | 时间序列预测 |
| 190 | [To See Far, Look Close: Evolutionary Forecasting for Long-term Time Series](https://neurips.cc/virtual/2026/poster/152940) | NEURIPS · 主会 | 时间序列预测 |
| 191 | [Multivariate Time Series Forecasting needs Cross Variable Loss](https://neurips.cc/virtual/2026/poster/150730) | NEURIPS · 主会 | 时间序列预测 |
| 192 | [Beyond Linear Decoders: Dynamic Expert-Coupled Optimal Decoding for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/153816) | NEURIPS · 主会 | 时间序列预测 |
| 193 | [Spectral Energy Allocation Enables Source-Free Domain Adaptation in Time Series Forecasting](https://neurips.cc/virtual/2026/oral/154915) | NEURIPS · 主会 | 时空与场景预测 |
| 194 | [Rethinking Post-Training Recipes for Multimodal Time-Series Forecasting](https://neurips.cc/virtual/2026/poster/153768) | NEURIPS · 主会 | 时间序列预测 |
| 195 | [Rubato: Signature Attention for Irregular Multivariate Time Series Forecasting](https://neurips.cc/virtual/2026/poster/153092) | NEURIPS · 主会 | 缺失与不规则时序 |
| 196 | [When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting](https://doi.org/10.1145/3770855.3819018) | KDD · 科学 AI | 基础模型与时序表征 |
| 197 | [SEER: Transformer-based Robust Time Series Forecasting via Automated Patch Enhancement and Replacement](https://proceedings.mlr.press/v306/qiu26n.html) | ICML | 时间序列预测 |
| 198 | [Beyond Model Ranking: Predictability-Aligned Evaluation for Time Series Forecasting](https://proceedings.mlr.press/v306/feng26u.html) | ICML | 评测、稳健性与不确定性 |
| 199 | [Tackling Time-Series Forecasting Generalization via Mitigating Concept Drift](https://proceedings.iclr.cc/paper_files/paper/2026/hash/96d328a1f6d8396d8c8a62f2beee252a-Abstract-Conference.html) | ICLR · 主会 | 时间序列预测 |
| 200 | [Robust Inter-Series Dependency Modeling for Time Series Forecasting via Information-Theoretic Alignment](https://proceedings.mlr.press/v306/yu26n.html) | ICML | 时间序列预测 |
| 201 | [Taming the Recent-Data Bias: Towards Robust Time Series Forecasting with Global Context](https://proceedings.mlr.press/v306/xu26ab.html) | ICML | 时间序列预测 |
| 202 | [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) | ICLR · 主会 | 缺失与不规则时序 |
| 203 | [Beyond Point Predictions: Manifold Expansion and Dual Alignment for Robust Time Series Distillation](https://proceedings.mlr.press/v306/hong26e.html) | ICML | 时间序列预测 |
| 204 | [Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting](https://proceedings.mlr.press/v306/han26v.html) | ICML | 评测、稳健性与不确定性 |
| 205 | [DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting](https://proceedings.mlr.press/v306/zhong26l.html) | ICML | 时间序列预测 |
| 206 | [ResCP: Reservoir Conformal Prediction for Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2026/hash/b0a978efeb20d33c15e31a5beee9f6df-Abstract-Conference.html) | ICLR · 主会 | 评测、稳健性与不确定性 |
| 207 | [Beyond Holistic Models: Systematic Component-level Benchmarking of Deep Multivariate Time-Series Forecasting](https://doi.org/10.1145/3770855.3817551) | KDD · 数据集与基准 | 评测、稳健性与不确定性 |
| 208 | [Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration](https://doi.org/10.1145/3770855.3817729) | KDD · 研究 | 评测、稳健性与不确定性 |
| 209 | [FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance](https://doi.org/10.1145/3770855.3817578) | KDD · 数据集与基准 | 评测、稳健性与不确定性 |
| 210 | [APT: Affine Prototype-Timestamp for Time Series Forecasting Under Distribution Shift](https://ojs.aaai.org/index.php/AAAI/article/view/38542) | AAAI · 技术论文 | 评测、稳健性与不确定性 |
| 211 | [Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventions](https://doi.org/10.1145/3770855.3817522) | KDD · 数据集与基准 | 评测、稳健性与不确定性 |
| 212 | [SEED: Spectral Entropy-Guided Evaluation of Spatial-Temporal Dependencies for Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/39930) | AAAI · 技术论文 | 评测、稳健性与不确定性 |
| 213 | [ReCast: Reliability-aware Codebook-assisted Lightweight Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/39610) | AAAI · 技术论文 | 评测、稳健性与不确定性 |
| 214 | [Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecasting](https://proceedings.mlr.press/v306/moretti26a.html) | ICML · 观点论文 | 评测、稳健性与不确定性 |
| 215 | [TimeMRA: LLM-Empowered Time Series Forecasting via Multi-Scale Retrieval-Augmented Representations](https://proceedings.mlr.press/v306/shang26b.html) | ICML | 基础模型与时序表征 |
| 216 | [Channel Adapter for Time Series Foundation Models in Zero-Shot Multivariate Forecasting](https://proceedings.mlr.press/v306/li26bu.html) | ICML | 基础模型与时序表征 |
| 217 | [Invariant Representation Learning for Source-Free Time Series Forecasting with LLM-Centric Proxy Denoising](https://proceedings.mlr.press/v306/yan26l.html) | ICML | 基础模型与时序表征 |
| 218 | [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) | ICML | 基础模型与时序表征 |
| 219 | [LagLLM: LLM-empowered lead–lag dependency learning for spatial-temporal time series forecasting](https://proceedings.mlr.press/v306/wu26ci.html) | ICML | 基础模型与时序表征 |
| 220 | [Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2026/hash/410dacbcfda0388f69b8983ed771c44e-Abstract-Conference.html) | ICLR · 主会 | 缺失与不规则时序 |
| 221 | [FireSentry: A Multi-Modal Spatio-temporal Benchmark Dataset for Fine-Grained Wildfire Spread Forecasting](https://doi.org/10.1145/3770854.3785696) | KDD · 数据集与基准 | 评测、稳健性与不确定性 |
| 222 | [Pre-training Epidemic Time Series Forecasters with Compartmental Prototypes](https://doi.org/10.1145/3770855.3818928) | KDD · 科学 AI | 基础模型与时序表征 |
| 223 | [LoFT-LLM: Low-Frequency Time-series Forecasting with Large Language Models](https://doi.org/10.1145/3770854.3780245) | KDD · 研究 | 基础模型与时序表征 |
| 224 | [Efficient Test-Time Scaling for LLM-based Time Series Forecasting](https://doi.org/10.1145/3770855.3818201) | KDD · 研究 | 基础模型与时序表征 |
| 225 | [Energy-Efficient Training-Free Zero-Inflation Correction for Rainfall Forecasting with Time-Series Foundation Models](https://doi.org/10.1145/3774904.3792998) | WWW · 论文类型待核 | 时空与场景预测 |
| 226 | [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) | AAAI · 技术论文 | 时间序列预测 |
| 227 | [M3Time: LLM-Enhanced Multi-Modal, Multi-Scale, and Multi-Frequency Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/39383) | AAAI · 技术论文 | 基础模型与时序表征 |
| 228 | [Unified Spatio-Temporal Tokens are Bases for Generalizable Traffic Forecasting](https://doi.org/10.1145/3770855.3817746) | KDD · 研究 | 时空与场景预测 |
| 229 | [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) | KDD · 研究 | 时间序列预测 |
| 230 | [Scalable Uncertainty Quantification for Extreme Weather Forecasting via Empirical Neural Tangent Kernels](https://doi.org/10.1145/3770855.3818106) | KDD · 研究 | 评测、稳健性与不确定性 |
| 231 | [GraFT: Infusing Pre-trained Transformers with Relational Structure for Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/40029) | AAAI · 技术论文 | 基础模型与时序表征 |
| 232 | [Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting](https://doi.org/10.1145/3770855.3818835) | KDD · 科学 AI | 基础模型与时序表征 |
| 233 | [RePatch: Learning Entropy-Guided Patch Structures with Quantized Representations for Time Series Forecasting](https://doi.org/10.1145/3770855.3817683) | KDD · 研究 | 时间序列预测 |
| 234 | [PatchCLE: Breaking the Linear Representation Bottleneck in Time Series Forecasting via Soft Contrastive Learning](https://doi.org/10.1145/3770855.3818023) | KDD · 研究 | 时间序列预测 |
| 235 | [TimeCAP: A Channel-Aware Pre-Training Framework for Multivariate Time Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/39700) | AAAI · 技术论文 | 基础模型与时序表征 |
| 236 | [TimeGuard: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting](https://proceedings.mlr.press/v306/nguyen26g.html) | ICML | 时间序列预测 |
| 237 | [Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecasting](https://proceedings.mlr.press/v306/qiu26t.html) | ICML | 缺失与不规则时序 |
| 238 | [L-Drive: Beyond a Single Mapping—Latent Context Drives Time Series Forecasting](https://proceedings.mlr.press/v306/zhang26p.html) | ICML | 时间序列预测 |
| 239 | [Reviving Error Correction in Modern Deep Time-Series Forecasting](https://proceedings.mlr.press/v306/nguyen26u.html) | ICML | 时间序列预测 |
| 240 | [KUMA: A Novel Framework with Koopman Separation and Efficient Multilevel Extraction in Time Series Forecasting](https://proceedings.mlr.press/v306/xiong26j.html) | ICML | 时间序列预测 |
| 241 | [KITE: Knowledge-Guided Probabilistic Modeling for Time Series Forecasting with Exogenous Variables](https://proceedings.mlr.press/v306/cheng26t.html) | ICML | 时间序列预测 |
| 242 | [FACT: Fine-grained Across-variable Convolution for Multivariate Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2026/hash/090d636f951ac0bb18fd5610c95c6a76-Abstract-Conference.html) | ICLR · 主会 | 时间序列预测 |
| 243 | [CPiRi: Channel Permutation-Invariant Relational Interaction for Multivariate Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2026/hash/ebba182cb97864368fdb6ae00773a5e4-Abstract-Conference.html) | ICLR · 主会 | 时间序列预测 |
| 244 | [ConFlux: Multivariate Time Series in Flux, One Unified Forecast in Confluence](https://proceedings.mlr.press/v306/wang26fy.html) | ICML | 时间序列预测 |
| 245 | [Revealing Scaling Paradox in Large-scale Time Series Models: Implications for More Efficient and Accurate Forecasting](https://proceedings.mlr.press/v306/qiu26q.html) | ICML | 时间序列预测 |
| 246 | [Dynamic TMoE: A Drift-Aware Dynamic Mixture of Experts Framework for Non-Stationary Time Series Forecasting](https://proceedings.mlr.press/v306/zhu26ad.html) | ICML | 时间序列预测 |
| 247 | [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) | ICLR · 主会 | 时间序列预测 |
| 248 | [Crisp: A Spectral-Based Interaction Strategy for Multivariate Time Series Forecasting](https://proceedings.mlr.press/v306/wang26ec.html) | ICML | 时间序列预测 |
| 249 | [PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting](https://proceedings.mlr.press/v306/liu26dn.html) | ICML | 时间序列预测 |
| 250 | [From Observations to States: Latent Time Series Forecasting](https://proceedings.mlr.press/v306/yang26y.html) | ICML | 时间序列预测 |
| 251 | [MoRGen: Mixture-of-Resolutions Generative Forecasting for Irregularly Sampled Medical Time-Series Data](https://proceedings.mlr.press/v306/oufattole26a.html) | ICML | 缺失与不规则时序 |
| 252 | [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) | ICLR · 主会 | 时间序列预测 |
| 253 | [ReNF: Rethinking the Design of Neural Long-Term Time Series Forecasters](https://proceedings.mlr.press/v306/lu26w.html) | ICML | 时间序列预测 |
| 254 | [StretchTime: Adaptive Time Series Forecasting via Symplectic Attention](https://proceedings.mlr.press/v306/kim26bk.html) | ICML | 时间序列预测 |
| 255 | [Byte Pair Encoding for Efficient Time Series Forecasting](https://proceedings.mlr.press/v306/gotz26a.html) | ICML | 时间序列预测 |
| 256 | [PHAT: Modeling Period Heterogeneity for Multivariate Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2026/hash/dbb5180957513805ebeea787b8c66ac9-Abstract-Conference.html) | ICLR · 主会 | 时间序列预测 |
| 257 | [FIPN: Forward Self-Organizing Interpretable Polynomial Networks for Time Series Forecasting](https://proceedings.mlr.press/v306/wang26hv.html) | ICML | 时间序列预测 |
| 258 | [PESD-TSF: A Period-Aware and Explicit Structured Decomposition Framework for Long-Term Time Series Forecasting](https://proceedings.mlr.press/v306/wang26cs.html) | ICML | 时间序列预测 |
| 259 | [SDHilb: Schrödinger Dynamics-Guided Neural Network with Adaptive Multi-Scale Hilbert Transform for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/152741) | NEURIPS · 主会 | 时间序列预测 |
| 260 | [DSENet: A Novel Dual-Stream Enhancement Network for Multi-Scale Non-Stationary Time Series Forecasting](https://proceedings.mlr.press/v306/wang26lu.html) | ICML | 时间序列预测 |
| 261 | [Ellipsoidal Time Series Forecasting](https://proceedings.mlr.press/v306/wang26ax.html) | ICML | 时间序列预测 |
| 262 | [Stability-Constrained Regime-Aware Forecasting for Heterogeneous Panel Time Series](https://neurips.cc/virtual/2026/poster/153846) | NEURIPS · 主会 | 时间序列预测 |
| 263 | [TimeSeed: Effective Time Series Forecasting with Sparse Endogenous Variables](https://proceedings.mlr.press/v306/wu26l.html) | ICML | 时间序列预测 |
| 264 | [Parameter Decorrelation via Transition-Variance Alignment for Multivariate Time-series Forecasting](https://proceedings.mlr.press/v306/choi26j.html) | ICML | 时间序列预测 |
| 265 | [Time–Frequency Non-Stationary Modeling for Multivariate Time Series Forecasting](https://neurips.cc/virtual/2026/poster/152338) | NEURIPS · 主会 | 时间序列预测 |
| 266 | [Information Geometry Loss for Time Series Forecasting](https://proceedings.mlr.press/v306/fang26m.html) | ICML | 时间序列预测 |
| 267 | [Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting](https://neurips.cc/virtual/2026/poster/148905) | NEURIPS · 主会 | 时间序列预测 |
| 268 | [Echoes of Error: Residual-Directional Local Rollout Consistency for Timeseries Forecasting](https://neurips.cc/virtual/2026/poster/152212) | NEURIPS · 主会 | 时间序列预测 |
| 269 | [Numerion: A Multi-Hypercomplex Model for Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2026/hash/5eca2e4fe7858cbbfef4e08573cfcb25-Abstract-Conference.html) | ICLR · 主会 | 时间序列预测 |
| 270 | [Anti-Aliasing Matters: A Dynamic Network for Time Series Forecasting](https://proceedings.mlr.press/v306/zhou26aq.html) | ICML | 时间序列预测 |
| 271 | [Online time series prediction using feature adjustment](https://proceedings.iclr.cc/paper_files/paper/2026/hash/f418594e90047a10f4c158f70d6701cc-Abstract-Conference.html) | ICLR · 主会 | 时间序列预测 |
| 272 | [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) | ICLR · 主会 | 时间序列预测 |
| 273 | [SpikeSTAG: A Dendritic Compartmental Spiking Graph Network for Multivariate Time-Series Forecasting](https://neurips.cc/virtual/2026/poster/153490) | NEURIPS · 主会 | 时间序列预测 |
| 274 | [AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/153106) | NEURIPS · 主会 | 时间序列预测 |
| 275 | [Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval](https://proceedings.iclr.cc/paper_files/paper/2026/hash/7a3284ce53f620d244fca0c79d94c478-Abstract-Conference.html) | ICLR · 主会 | 时间序列预测 |
| 276 | [Not All Frequencies Are Equal: Energy-Adaptive Diffusion for Time Series Forecasting](https://proceedings.mlr.press/v306/qin26e.html) | ICML | 时空与场景预测 |
| 277 | [Hi-Time: Hierarchical Latent Prediction for Multivariate Time Series Classification](https://proceedings.mlr.press/v306/zeng26s.html) | ICML | 时序分类 |
| 278 | [DistDF: Time-series Forecasting Needs Joint-distribution Wasserstein Alignment](https://proceedings.iclr.cc/paper_files/paper/2026/hash/d591ef4c631b33ac4e08dd88f1ee1dff-Abstract-Conference.html) | ICLR · 主会 | 时间序列预测 |
| 279 | [MDK-MoE: Multi-view Decomposed Kalman Mixture of Experts Framework for Non-stationary Time Series Forecasting](https://neurips.cc/virtual/2026/poster/151139) | NEURIPS · 主会 | 时间序列预测 |
| 280 | [Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting](https://proceedings.mlr.press/v306/li26fu.html) | ICML | 时间序列预测 |
| 281 | [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) | ICLR · 主会 | 时间序列预测 |
| 282 | [IMTS-Tokenizer: Time-Aware Tokenization for Irregular Multivariate Time Series Forecasting](https://neurips.cc/virtual/2026/poster/154661) | NEURIPS · 主会 | 缺失与不规则时序 |
| 283 | [MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning](https://proceedings.mlr.press/v306/tao26a.html) | ICML | 时间序列预测 |
| 284 | [Nested Spatio-Temporal Time Series Forecasting](https://proceedings.mlr.press/v306/ai26f.html) | ICML | 时间序列预测 |
| 285 | [MSAR: Next-Scale Autoregressive Forecasting for Time Series via Modular Multi-Scale Decoupling](https://neurips.cc/virtual/2026/poster/149231) | NEURIPS · 主会 | 时间序列预测 |
| 286 | [ToLD: Efficient Time Series Forecasting via Tokenized Truncated Latent Diffusion](https://neurips.cc/virtual/2026/poster/156116) | NEURIPS · 主会 | 时间序列预测 |
| 287 | [ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting](https://proceedings.iclr.cc/paper_files/paper/2026/hash/65e17c5eb95d72ad6540e8f862f38ab1-Abstract-Conference.html) | ICLR · 主会 | 时间序列预测 |
| 288 | [DiffPTS: Rethinking Diffusion ELBO for Probabilistic Time Series Forecasting](https://neurips.cc/virtual/2026/poster/148542) | NEURIPS · 主会 | 时间序列预测 |
| 289 | [Koopman Generative Operators for Efficient Probabilistic Time-Series Forecasting](https://neurips.cc/virtual/2026/poster/153665) | NEURIPS · 主会 | 时间序列预测 |
| 290 | [Probabilistic Circuits for Irregular Multivariate Time Series Forecasting](https://neurips.cc/virtual/2026/poster/152394) | NEURIPS · 主会 | 缺失与不规则时序 |
| 291 | [Beyond Extrapolation: Knowledge Utilization Paradigm with Bidirectional Inspiration for Time Series Forecasting](https://proceedings.mlr.press/v306/chong26c.html) | ICML | 时间序列预测 |
| 292 | [Local Geometry Attention for Time Series Forecasting under Realistic Corruptions](https://proceedings.iclr.cc/paper_files/paper/2026/hash/476ab8f369e489c04187ba84f68cfa68-Abstract-Conference.html) | ICLR · 主会 | 时间序列预测 |
| 293 | [TimeSeg: An Information-Theoretic Segment-Wise Explainer for Time-Series Predictions](https://proceedings.iclr.cc/paper_files/paper/2026/hash/f4fba41b554f9aaa013c4062a1c40518-Abstract-Conference.html) | ICLR · 主会 | 时间序列预测 |
| 294 | [WILD: Widely Linear Conditioning for Time Series Forecasting](https://neurips.cc/virtual/2026/poster/148890) | NEURIPS · 主会 | 时间序列预测 |
| 295 | [CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Models](https://proceedings.mlr.press/v306/wang26ay.html) | ICML | 时间序列预测 |
| 296 | [What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions](https://proceedings.mlr.press/v306/gu26r.html) | ICML | 时间序列预测 |
| 297 | [DSR-TSF: Spectrum-Driven Dynamic Routing for Efficient Long-Horizon Time Series Forecasting](https://neurips.cc/virtual/2026/poster/152845) | NEURIPS · 主会 | 时间序列预测 |
| 298 | [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) | ICLR · 主会 | 时间序列预测 |
| 299 | [SteerCast: Retrieval-Based Latent Steering for Decoder-Only Time Series Forecasting](https://neurips.cc/virtual/2026/poster/154457) | NEURIPS · 主会 | 时间序列预测 |
| 300 | [Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models](https://proceedings.iclr.cc/paper_files/paper/2026/hash/d56a1a2b24b1906345fc2cd8276b11ed-Abstract-Conference.html) | ICLR · 主会 | 时间序列预测 |

## 数据与复查材料

- [固定版本 360 篇库存](候选数据/已有论文_inventory_360.json)；[首轮审计 345 篇集合](候选数据/首轮345论文_站点集合候选.json)。后者与在线完整集合是否完全相同仍待核对。
- [最终 300 篇结构化清单](候选数据/待审300.json)、[CSV](候选数据/待审300.csv)；其中保留会议、来源链接、部分轨道和粗筛分数。
- [合并初筛候选](候选数据/合并初筛.json)、[去重记录](候选数据/去重记录.csv)；各会议原始筛选数据均位于 `候选数据/`。
- [筛选与去重脚本](筛选与去重.py)、[300 篇选择脚本](编制300篇.py)、[报告生成脚本](生成报告.py)。筛选分数仅服务于覆盖与选题优先级，不是论文质量或可信度评分。