VLDB 2025 | 时间序列（Time Series）论文总结（预测，异常检测，数据压缩，自动化等）

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发布于 2026-03-10 14:51:03

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VLDB 2025于2025年9月1号-5号在英国伦敦(London, United Kingdom)举行。

本文总结了VLDB 2025有关时间序列（Time Series）的相关论文，主要包含如有疏漏，欢迎大家补充。

时间序列Topic：预测，异常检测，聚类，数据压缩，自动化，大模型，时序数据库等。

1. Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching2. A Memory Guided Transformer for Time Series Forecasting3. Goku: A Schemaless Time Series Database for Large Scale Monitoring at Pinterest4. Fully Automated Correlated Time Series Forecasting in Minutes5. Discovering Leitmotifs in Multidimensional Time Series6. MLP-Mixer based Masked Autoencoders Are Effective, Explainable and Robust for Time Series Anomaly Detection7. Representative Time Series Discovery for Data Exploration8. Migration-Free Elastic Storage of Time Series in Apache IoTDB9. Streaming Time Series Subsequence Anomaly Detection: A Glance and Focus Approach10. Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains11. Time Series Motif Discovery: A Comprehensive Evaluation12. ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning13. STsCache: An Efficient Semantic Caching Scheme for Time-series Data Workloads Based on Hybrid Storage14. TAB: Unified Benchmarking of Time Series Anomaly Detection Methods15. UFGTime: Mining Intertwined Dependencies in Multivariate Time Series via an Efficient Pure Graph Approach (Flavor: Foundations and Algorithms Papers)16. MOMENTI: Scalable Motif Mining in Multidimensional Time Series17. Improving Time Series Data Compression in Apache IoTDB18. TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection [E, A & B]19. Time-Series Clustering: A Comprehensive Study of Data Mining, Machine Learning, and Deep Learning Methods20. Demonstration of ModelarDB: Model-Based Management of High-Frequency Time Series Across Edge, Cloud, and Client21. EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection22. SAIL: A Voyage to Symbolic Approximation Solutions for Time-Series Analysis

1 Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching

链接：https://www.vldb.org/pvldb/vol18/p226-miao.pdf

代码：https://github.com/uestc-liuzq/STdistillation

作者：Hao Miao, Ziqiao Liu, Yan Zhao, Chenjuan Guo, Bin Yang, Kai Zheng, Christian S. Jensen

关键词：时序数据压缩，模态匹配

2 A Memory Guided Transformer for Time Series Forecasting

链接：https://www.vldb.org/pvldb/vol18/p239-cheng.pdf

代码：https://github.com/YunyaoCheng/Memformer

作者：Yunyao Cheng, Chenjuan Guo, Bin Yang, Haomin Yu, Kai Zhao, Christian S. Jensen

关键词：预测，Transformer，记忆

3 Goku: A Schemaless Time Series Database for Large Scale Monitoring at Pinterest

链接：https://www.vldb.org/pvldb/vol18/p503-sanghavi.pdf

作者：Monil Mukesh Sanghavi, Ming-May Hu, Zhenxiao Luo, Xiao Li, Kapil Bajaj

关键词：无模式时序数据库

4 Fully Automated Correlated Time Series Forecasting in Minutes

链接：https://www.vldb.org/pvldb/vol18/p144-wu.pdf

代码：https://github.com/ccloud0525/FACTS

作者：Xinle Wu, Xingjian Wu, Dalin Zhang, Miao Zhang, Chenjuan Guo, Bin Yang, Christian S. Jensen

关键词：全自动化预测，效率

5 Discovering Leitmotifs in Multidimensional Time Series

链接：https://www.vldb.org/pvldb/vol18/p377-schafer.pdf

代码：https://github.com/patrickzib/leitmotifs

作者：Patrick Schäfer, Ulf Leser

关键词：主导动机发现,多维时间序列

6 MLP-Mixer based Masked Autoencoders Are Effective, Explainable and Robust for Time Series Anomaly Detection

链接：https://www.vldb.org/pvldb/vol18/p798-qideng.pdf

代码：https://github.com/richard-tang199/MMA

作者：Tang Qideng, Dai Chaofan, Wu Yahui, Zhou Haohao

关键词：异常检测，MAE，MLP-Mixer

7 Representative Time Series Discovery for Data Exploration

链接：https://www.vldb.org/pvldb/vol18/p915-bao.pdf

代码：https://github.com/rmitbggroup/RTSD

作者：Ge Lee, Shixun Huang, Zhifeng Bao, Yanchang Zhao

关键词：时序相似度度量

8 Migration-Free Elastic Storage of Time Series in Apache IoTDB

链接：https://www.vldb.org/pvldb/vol18/p1784-song.pdf

作者：Rongzhao Chen, Xiangpeng Hu, Xiangdong Huang, Chen Wang, Shaoxu Song, Jianmin Wang

关键词：弹性时序存储，IoTDB

9 Streaming Time Series Subsequence Anomaly Detection: A Glance and Focus Approach

链接：https://www.vldb.org/pvldb/vol18/p1892-zheng.pdf

代码：https://github.com/Wangwenjing1996/Sirloin

作者：Wenjing Wang, Ziyang Yue, Bolong Zheng

关键词：异常检测，流式时序

10 Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains

链接：https://www.vldb.org/pvldb/vol18/p1691-jacob.pdf

代码：https://github.com/exathlonbenchmark/divad

作者：Vincent Jacob, Yanlei Diao

关键词：异常检测，无监督，异构域

11 Time Series Motif Discovery: A Comprehensive Evaluation

链接：https://www.vldb.org/pvldb/vol18/p2226-boniol.pdf

代码：https://github.com/grrvlr/TSMD

作者：Valerio Guerrini, Thibaut Germain, Charles Truong, Laurent Oudre, Paul Boniol

关键词：模式（基序）发现

12 ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning

链接：https://www.vldb.org/pvldb/vol18/p2385-xie.pdf

代码：https://github.com/NetManAIOps/ChatTS

作者：Zhe Xie, Zeyan Li, Xiao He, Longlong Xu, Xidao Wen, Tieying Zhang, Jianjun Chen, Rui Shi, Dan Pei

关键词：LLM，时序推理，对齐，智能运维

13 STsCache: An Efficient Semantic Caching Scheme for Time-series Data Workloads Based on Hybrid Storage

链接：https://www.vldb.org/pvldb/vol18/p2964-li.pdf

代码：https://github.com/ts-lab1024/ts-semantic-caching

作者：Tao Kong, Hui Li, Yuxuan Zhao, Liping Li, Xiyue Gao, Qilong Wu, Jiangtao Cui

关键词：时间序列数据工作负载，查询模式

14 TAB: Unified Benchmarking of Time Series Anomaly Detection Methods

链接：https://www.vldb.org/pvldb/vol18/p2775-hu.pdf

代码：https://github.com/decisionintelligence/TAB

作者：Xiangfei Qiu, Zhe Li, Wanghui Qiu, Shiyan Hu, Lekui Zhou, Xingjian Wu, Zhengyu Li, Chenjuan Guo, Aoying Zhou, Zhenli Sheng, Jilin Hu, Christian S. Jensen, Bin Yang

关键词：异常检测，benchmark

15 UFGTime: Mining Intertwined Dependencies in Multivariate Time Series via an Efficient Pure Graph Approach (Flavor: Foundations and Algorithms Papers)

链接：https://www.vldb.org/pvldb/vol18/p3175-gao.pdf

代码：https://github.com/WonderHeiYi/UFGTIME

作者：Ruikun Li, Dai Shi, Ye Xiao, Junbin Gao

关键词：多元时序预测，GNN

16 MOMENTI: Scalable Motif Mining in Multidimensional Time Series

链接：https://www.vldb.org/pvldb/vol18/p3463-ceccarello.pdf

代码：https://github.com/aidaLabDEI/MOMENTI-motifs

作者：Matteo Ceccarello, Francesco Pio Monaco, Francesco Silvestri

关键词：基序挖掘，多维时序

17 Improving Time Series Data Compression in Apache IoTDB

链接：https://www.vldb.org/pvldb/vol18/p3406-tang.pdf

代码：https://github.com/yuxin370/CompressIoTDB

作者：Yuxin Tang, Feng Zhang, Jiawei Guan, Yuan Tian, Xiangdong Huang, Chen Wang, Jianmin Wang, Xiaoyong Du

关键词：时序数据压缩，模态匹配

18 TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection [E, A & B]

链接：https://www.vldb.org/pvldb/vol18/p4364-liu.pdf

代码：https://github.com/TheDatumOrg/TSB-AutoAD

作者：Qinghua Liu, Seunghak Lee, Paparrizos John

关键词：异常检测，自动化

19 Time-Series Clustering: A Comprehensive Study of Data Mining, Machine Learning, and Deep Learning Methods

链接：https://www.vldb.org/pvldb/vol18/p4380-paparrizos.pdf

代码：http://www.timeseries.org/tsclusteringeval

作者：John Paparrizos, Sai Prasanna Teja Reddy Bogireddy

关键词：时序聚类

20 Demonstration of ModelarDB: Model-Based Management of High-Frequency Time Series Across Edge, Cloud, and Client

链接：https://www.vldb.org/pvldb/vol18/p5247-jensen.pdf

作者：Søren Kejser Jensen, Christian Schmidt Godiksen, Christian Thomsen, Torben Bach Pedersen

关键词：边缘部署，ModelarDB

21 EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection

链接：https://www.vldb.org/pvldb/vol18/p5431-liu.pdf

作者：Qinghua Liu, Seunghak Lee, John Paparrizos

关键词：异常检测，自动化解决方案

22 SAIL: A Voyage to Symbolic Approximation Solutions for Time-Series Analysis

链接：https://www.vldb.org/pvldb/vol18/p5419-yang.pdf

代码：https://github.com/TheDatumOrg/SAIL

作者：Fan Yang, John Paparrizos

关键词：时序分析，符号分解