当前位置：首页 > news >正文

ECML PKDD 2025 | 时间序列（Time Series）论文总结

news 2025/8/13 15:14:29

ECMLP KDD是CCF B类会议。ECML PKDD2025将在2025年9月15号到19号在加葡萄牙波尔图（
Porto, Portugal）举行，本文总结了ECMLPKDD2025有关时间序列（Time Series）相关文章，共计14篇，其中1-11为Research Track，12-14为ADS Track。

时间序列Topic：预测,分类,异常检测,生成,可解释性等。如有疏漏，欢迎补充！

Research Track
1. An Empirical Evaluation of Foundation Models for Multivariate Time Series Classification
2. Bridging Neural Networks and Dynamic Time Warping for Adaptive Time Series Classification
3. Cross-Domain Conditional Diffusion Models for Time Series Imputation
4. Federated Time Series Generation on Feature and Temporally Misaligned Data
5. G-GLformer: Transformer with GRU Embedding and Global-Local Attention for Multivariate Time Series Forecasting
6. MASCOTS: Model-Agnostic Symbolic COunterfactual explanations for Time Series
7. MotiPlus and MotiSet: Discovering the Best Set of Motiflets in Time Series
8. Multivariate Time Series Anomaly Prediction Based on Forecasting and Reconstruction Using Transformer with Temporal and Feature-wise Attention
9. RandomAD: A Random Kernel-based Anomaly Detector for Time Series
10. Right on Time: Revising Time Series Models by Constraining their Explanations
11. TSHAP: Fast and Exact SHAP for Explaining Time Series Classification and Regression
ADS Track
12. Forecasting Irregularly Sampled Time Series with Transformer Encoders
13. InterDiff: Synthesizing Financial Time Series with Inter-Stock Correlations via Classifier-Free Guided Diffusion
14. Ordinal Aligned Domain Generalization for Sensor-based Time Series Regression

🌟【紧跟前沿】“时空探索之旅”与你一起探索时空奥秘！🚀
欢迎大家关注时空探索之旅时空探索之旅在这里插入图片描述

1 An Empirical Evaluation of Foundation Models for Multivariate Time Series Classification

作者：Pinar Sungu Isiacik (University College Dublin)*; Thach Le Nguyen (University College Dublin); Timilehin Aderinola (University College Dublin); Georgiana Ifrim (University College Dublin)

关键词：分类，基础模型

2 Bridging Neural Networks and Dynamic Time Warping for Adaptive Time Series Classification

作者：Jintao Qu (University of Southern California)*; Zichong Wang (Florida International University); Chenhao Wu ( University of Southern California); Wenbin Zhang ( Florida International University); Dongmei Li (Beijing Forestry University)

关键词：分类，自适应

3 Cross-Domain Conditional Diffusion Models for Time Series Imputation

代码：https://github.com/kexin-kxzhang/CD2-TSI

作者：Kexin Zhang (Northwestern University)*; Baoyu Jing (University of Illinois Urbana-Champaign); Selcuk Candan (Arizona State University); Dawei Zhou (Virginia Tech); Qingsong Wen (Squirrel Ai Learning); Han Liu (Northwestern University); Kaize Ding (Northwestern University)

关键词：插补，域适应，扩散模型

4 Federated Time Series Generation on Feature and Temporally Misaligned Data

作者：Zhi Wen Soi (University of Bern); Chenrui Fan (University of Bern); Aditya Shankar (TU Delft); Abel Malan (University of Neuchatel); Lydia Chen (University of Neuchatel)*

关键词：生成，联邦

5 G-GLformer: Transformer with GRU Embedding and Global-Local Attention for Multivariate Time Series Forecasting

作者：Wenjun Yu (Shanghai University of International Business and Economics)*; Jiyanglin Li (Guizhou Key Laboratory of Big Data Statistical Analysis; Guizhou University of Finance and Economics); Wentao Gao (University of South Australia); Niangxi Zhuang (Guangzhou Nanfang College); Wen Li (Shanghai University of International Business and Economics); Shouguo Du (Shanghai Municipal Big Data Center)

关键词：预测，多元时间序列

6 MASCOTS: Model-Agnostic Symbolic COunterfactual explanations for Time Series

作者：Dawid P_udowski (Warsaw University of Technology)*; Francesco Spinnato (University of Pisa); Piotr Wilczy_ski (ETH ZÃ¼rich); Krzysztof Kotowski (KP Labs); Evridiki Ntagiou (European Space Operations Centre); Riccardo Guidotti (University of Pisa); Przemys_aw Biecek (Warsaw University of Technology)

关键词：反事实解释，可解释性

7 MotiPlus and MotiSet: Discovering the Best Set of Motiflets in Time Series

作者：Len Feremans (Universiteit Antwerpen)*; Patrick SchÃ¤fer (Humboldt-University at Berlin); Wannes Meert (KU Leuven)

关键词：模式发现

8 Multivariate Time Series Anomaly Prediction Based on Forecasting and Reconstruction Using Transformer with Temporal and Feature-wise Attention

作者：Chihiro Maru (Chuo University)*; Masato Oguchi (Ochanomizu University); Ichiro Kobayashi (Ochanomizu University)

关键词：异常预测，多变量时间序列预测

9 RandomAD: A Random Kernel-based Anomaly Detector for Time Series

作者：Wenjie Xi (George Mason University)*; Jessica Lin (George Mason University)

关键词：异常检测，卷积

10 Right on Time: Revising Time Series Models by Constraining their Explanations

作者：Maurice Kraus (TU-Darmstadt)*; David Steinmann (TU-Darmstadt); Antonia WÃ¼st (TU-Darmstadt); Andre Kokozinski (TU-Darmstadt); Kristian Kersting (TU-Darmstadt)

关键词：时频交互，“聪明汉斯” 现象

11 TSHAP: Fast and Exact SHAP for Explaining Time Series Classification and Regression

作者：Thach Le Nguyen (University College Dublin)*; Georgiana Ifrim (University College Dublin)

关键词：可解释性，评测

12 Forecasting Irregularly Sampled Time Series with Transformer Encoders

代码：https://github.com/softlab-unimore/ISTF

作者：Riccardo Benassi (University of Modena and Reggio Emilia); Francesco Del Buono (University of Modena and Reggio Emilia); Giacomo Guiduzzi ( University of Modena and Reggio Emilia); Francesco Guerra (University of Modena e Reggio Emilia)*

关键词：不规则时序预测

13 InterDiff: Synthesizing Financial Time Series with Inter-Stock Correlations via Classifier-Free Guided Diffusion

作者：Hou-Wan Long (The Chinese University of Hong Kong)*; Zoufei Tang (Super Quantum Capital Management); Jianhui Zhang (Super Quantum Capital Management); Zhuoyang Zhan (Super Quantum Capital Management); Tao Lu (Southern University of Science and Technology); Xiaoquan Zhang (Tsinghua University)

关键词：股票预测，数据增强，扩散模型

14 Ordinal Aligned Domain Generalization for Sensor-based Time Series Regression

代码：https://github.com/yshi22/OATSDG

作者：Yunchuan Shi (The University of Sydney)*; Wei Li (The University of Sydney); Albert Zomaya (The University of Sydney)

关键词：域泛化，时间序列回归，序数对齐，标签空间移位。