WWW 2026将在2026年4月13日到17日于阿联酋迪拜（Dubai, United Arab Emirates）举行。
本文总结了WWW 2026上有关时间序列（time series）的相关论文，包含Research，industry和Web4Good3个Track的论文，总计27篇，如有疏漏，欢迎补充。
Research Track录用列表：https://www2026.thewebconf.org/accepted/research-tracks.html
Web4Good Track录用列表：https://www2026.thewebconf.org/accepted/web4good.html
Web4Good的介绍（Call for Papers, CFP）：https://www2026.thewebconf.org/calls/web4good.html（这个是收录了proceeding的，各个学校对该track是否是CCF A的认定，欢迎大家补充相关信息）
时间序列Topic：预测，分类，异常检测，插补，生成等任务，以及LLM，MLLM，VLM，KAN和Mamba等技术在上述任务中的应用。
| Research 1. EEO-TFV: Escape-Explore Optimizer for Web-Scale Time-Series Forecasting and Vision Analysis 2. Enhancing Large Language Models for Time-Series Forecasting via Vector-Injected In-Context Learning 3. Cross-city Time Series Forecasting with Retrieval-Augmented Large Language Models 4. Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification 5. Automated Model Selection for Multivariate Time Series Forecasting 6. SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting 7. Dynamic Multi-period Experts for Online Time Series Forecasting 8. Efficient High-Dimensional Time Series Forecasting with Transformers: A Channel Reordering Perspective 9. Re-Diffusion: Modeling Latent Residuals with Diffusion for Time-Series Forecasting 10. Time-TK: A Multi-Offset Temporal Interaction Framework Combining Transformer and Kolmogorov-Arnold Networks for Time Series Forecasting 11. FedRMamba: Federated Residual Mamba for Multivariate Time-Series Forecasting 12. QuiZSF: A Retrieval-Augmented Framework for Zero-Shot Time Series Forecasting 13. GFMixer: Decoupled Temporal Gradient and Fourier-Aware Attention for Time Series Forecasting 14. Byte-token Enhanced Language Models for Temporal Point Processes Analysis 15. FSDI: Frequency-Shaped Diffusion For Time-Series Imputation 16. TimeMar: Multi-Scale Autoregressive Modeling for Unconditional Time Series Generation 17. Can Multimodal LLMs Perform Time Series Anomaly Detection? 18. ViTs: Teaching Machines to See Time Series Anomalies Like Human Experts 19. Evolving Proxy Kills Drift: Data-Efficient Streaming Time Series Anomaly Detection 20. FedDiG: Frequency-Guided Diffusion Diversity for Generalizable Federated Time Series Classification 21. We Need a More Robust Classifier: Dual Causal Learning Empowers Domain-Incremental Time Series Classification 22. Predictability-Aware Compression and Decompression Framework for Multichannel Time Series Data with Latent Seasonality 23. Interpretable Dynamic Network Modeling of Tensor Time Series via Kronecker Time-Varying Graphical Lasso 24. Lifting Manifolds to Mitigate Pseudo-Alignment in LLM4TS Industry 25. Delay-Aware Graph Neural Stochastic Differential Equations for Financial Time Series Modeling and Forecasting Web4Good 26. Toward Green Computing: General Carbon Intensity Forecasting via Dual Graph Empowered Time Series Foundation Model 27. Energy-Efficient Training-Free Zero-Inflation Correction for Rainfall Forecasting with Time-Series Foundation Models |
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Research
1 EEO-TFV: Escape-Explore Optimizer for Web-Scale Time-Series Forecasting and Vision Analysis
链接 ：++https://arxiv.org/abs/2602.02551++
作者：Hua Wang, Jinghao Lu and Fan Zhang
关键词：预测，优化器
2 Enhancing Large Language Models for Time-Series Forecasting via Vector-Injected In-Context Learning
链接 ：++https://arxiv.org/abs/2601.07903++
作者：Jianqi Zhang, Jingyao Wang, Wenwen Qiang, Fanjiang Xu and Changwen Zheng
关键词：预测，LLM，上下文学习
3 Cross-city Time Series Forecasting with Retrieval-Augmented Large Language Models
链接 ：++https://arxiv.org/abs/2508.18635++
作者：Yue Jiang, Chenxi Liu, Yile Chen, Qin Chao, Shuai Liu, Cheng Long and Gao Cong
关键词：跨城市时空预测，LLM，迁移学习
4 Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification
链接 ：++https://arxiv.org/abs/2602.16224++
作者：Xu Zhang, Peng Wang, Yichen Li and Wei Wang
关键词：预测，分类，摊销
5 Automated Model Selection for Multivariate Time Series Forecasting
作者：Xiaoxuan Fan, Jiaqi Sun, Xianjun Deng, Qiankun Zhang, Wei Xiang, Shenghao Liu and Lingzhi Yi
关键词：预测，模型选择
6 SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting
链接 ：++https://arxiv.org/abs/2602.16220++
作者：Xu Zhang, Qitong Wang, Peng Wang and Wei Wang
关键词：长时预测，MLP
7 Dynamic Multi-period Experts for Online Time Series Forecasting
作者：Seungha Hong, Sukang Chae, Suyeon Kim, Sanghwan Jang and Hwanjo Yu
关键词：在线预测，混合专家
8 Efficient High-Dimensional Time Series Forecasting with Transformers: A Channel Reordering Perspective
作者：Yuchen Fang, Shiyu Wang, Yuxuan Liang, Zhou Ye, Yang Xiang, Yan Zhao and Kai Zheng
关键词：高维预测，通道重排序
9 Re-Diffusion: Modeling Latent Residuals with Diffusion for Time-Series Forecasting
作者：Boning Zhang, Haishuai Wang, Zehong Hu, Jiajun Wang, Hongyi Zhang and Jia Jia
关键词：预测，扩散模型，残差
10 Time-TK: A Multi-Offset Temporal Interaction Framework Combining Transformer and Kolmogorov-Arnold Networks for Time Series Forecasting
链接 ：++https://arxiv.org/abs/2602.11190++
作者：Fan Zhang, Shiming Fan and Hua Wang
关键词：预测，KAN，表示学习
11 FedRMamba: Federated Residual Mamba for Multivariate Time-Series Forecasting
作者：Zhiwei Hu, Liang Zhang and Guangxu Zhu
关键词：预测，联邦学习，Mamba
12 QuiZSF: A Retrieval-Augmented Framework for Zero-Shot Time Series Forecasting
链接 ：++https://arxiv.org/abs/2508.06915++
作者：Shichao Ma, Zhengyang Zhou, Qihe Huang, Binwu Wang and Yang Wang
关键词：零样本预测，RAG
13 GFMixer: Decoupled Temporal Gradient and Fourier-Aware Attention for Time Series Forecasting
作者：Lin Zhang, Qing Li and Jingmei Zhao
关键词：预测，时域，频域
14 Byte-token Enhanced Language Models for Temporal Point Processes Analysis
链接 ：++https://arxiv.org/abs/2502.07139++
作者：Quyu Kong, Yixuan Zhang, Yang Liu, Panrong Tong, Enqi Liu and Feng Zhou
关键词：时间点过程，大模型，事件分析
15 FSDI: Frequency-Shaped Diffusion For Time-Series Imputation
作者：Wangmeng Shen, Hongfan Gao, Qingsong Zhong, Dingli Xu and Jilin Hu
关键词：插补，扩散，频域
16 TimeMar: Multi-Scale Autoregressive Modeling for Unconditional Time Series Generation
链接 ：++https://arxiv.org/abs/2601.11184++
作者：Xiangyu Xu, Qingsong Zhong and Jilin Hu
关键词：无条件时序生成，自回归
17 Can Multimodal LLMs Perform Time Series Anomaly Detection?
链接 ：++https://arxiv.org/abs/2502.17812++
作者：Xiongxiao Xu, Haoran Wang, Yueqing Liang, Philip S. Yu, Yue Zhao and Kai Shu
关键词：异常检测，多模态大模型
18 ViTs: Teaching Machines to See Time Series Anomalies Like Human Experts
链接 ：++https://arxiv.org/abs/2510.04710++
作者：Zexin Wang, Changhua Pei, Yang Liu, Hengyue Jiang, Quan Zhou, Haotian Si, Hang Cui, Jianhui Li, Gaogang Xie, Jingjing Li and Dan Pei
关键词：异常检测，视觉模型
19 Evolving Proxy Kills Drift: Data-Efficient Streaming Time Series Anomaly Detection
作者：Qing Wei, Hao Miao, Yan Zhao, Kai Zheng, Bin Yang, Volker Markl and Christian S. Jensen
关键词：异常检测，流式数据
20 FedDiG: Frequency-Guided Diffusion Diversity for Generalizable Federated Time Series Classification
作者：Haoran Shi, Junru Zhang, Cheng Peng, Xiaoli Tang, Longtao Huang and Han Yu
关键词：分类，频域，联邦
21 We Need a More Robust Classifier: Dual Causal Learning Empowers Domain-Incremental Time Series Classification
链接 ：++https://arxiv.org/abs/2601.10312++
作者：Zhipeng Liu, Peibo Duan, Xuan Tang, Haodong Jing, Mingyang Geng, Yongsheng Huang, Jialu Xu, Bin Zhang and Binwu Wang
关键词：分类，增量学习，稳健性
22 Predictability-Aware Compression and Decompression Framework for Multichannel Time Series Data with Latent Seasonality
链接 ：++https://arxiv.org/abs/2506.00614++
作者：Ziqi Liu, Pei Zeng and Yi Ding
关键词：季节性，压缩和解压
23 Interpretable Dynamic Network Modeling of Tensor Time Series via Kronecker Time-Varying Graphical Lasso
链接 ：++https://arxiv.org/abs/2602.08197++
作者：Shingo Higashiguchi, Koki Kawabata, Yasuko Matsubara and Yasushi Sakurai
关键词：张量时间序列、图形套索、网络推理
24 Lifting Manifolds to Mitigate Pseudo-Alignment in LLM4TS
链接 ：++https://arxiv.org/abs/2510.12847++
作者：Liangwei Nathan Zheng, Wenhao Liang, Wei Emma Zhang, Miao Xu, Olaf Maennel and Weitong Chen
关键词：LLM，伪对齐
Industry
25 Delay-Aware Graph Neural Stochastic Differential Equations for Financial Time Series Modeling and Forecasting
作者：Mingjie You:Tongji University;Dawei Cheng:Tongji University;Meilin Zhang:Tongji University;Peng Zhu:Tongji University;Yuqi Liang:Seek Data Group, Emoney Inc.
关键词：金融时序建模，图随机微分方程
Web4Good
26 Toward Green Computing: General Carbon Intensity Forecasting via Dual Graph Empowered Time Series Foundation Model
作者：Xiaoyang Zhang:Hong Kong Polytechnic University;Taiqi Zhou:Hong Kong Polytechnic University;Fang He:Hong Kong Polytechnic University;Yang Deng:Hong Kong Polytechnic University;Dan Wang:Hong Kong University of Science and Technology
关键词：基础模型，绿色计算
27 Energy-Efficient Training-Free Zero-Inflation Correction for Rainfall Forecasting with Time-Series Foundation Models
作者：Wentao Gao:Adelaide University;Xiaojing Du:Adelaide University;Xiongren Chen:Adelaide University;Yifan Guo:Adelaide University;Andres Mauricio Cifuentes-Bernal:Adelaide University;Renqiang Luo:Jilin University;Ziqi Xu:RMIT University
关键词：降雨预测，基础模型，节能