Joint Time Series Chain: Detecting Unusual Evolving Trend across Time Series

2026-02-14

SDM

AI Summary

Existing time series chain methods are confined to individual sequences and struggle to capture anomalous evolution patterns across interruptions or related sequences. This work proposes a novel formulation—Joint Time Series Chain—that extends the concept of time series chains to cross-sequence scenarios for the first time. By leveraging subsequence similarity modeling, a cross-sequence alignment strategy, and a new chain-ranking criterion, the method efficiently uncovers robust anomalous evolution trends. Empirical evaluations demonstrate that the proposed approach significantly outperforms current state-of-the-art techniques across multiple datasets. Furthermore, it has been successfully deployed in an Intel manufacturing setting, where it effectively identifies complex anomalous patterns, showcasing its practical utility in real-world industrial applications.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Time series chain (TSC) is a recently introduced concept that captures the evolving patterns in large scale time series. Informally, a time series chain is a temporally ordered set of subsequences, in which consecutive subsequences in the chain are similar to one another, but the last and the first subsequences maybe be dissimilar. Time series chain has the great potential to reveal latent unusual evolving trend in the time series, or identify precursor of important events in a complex system. Unfortunately, existing definitions of time series chains only consider finding chains in a single time series. As a result, they are likely to miss unexpected evolving patterns in interrupted time series, or across two related time series. To address this limitation, in this work, we introduce a new definition called \textit{Joint Time Series Chain}, which is specially designed for the task of finding unexpected evolving trend across interrupted time series or two related time series. Our definition focuses on mitigating the robustness issues caused by the gap or interruption in the time series. We further propose an effective ranking criterion to identify the best chain. We demonstrate that our proposed approach outperforms existing TSC work in locating unusual evolving patterns through extensive empirical evaluations. We further demonstrate the utility of our work with a real-life manufacturing application from Intel. Our source code is publicly available at the supporting page https://github.com/lizhang-ts/JointTSC .

Problem

• Time Series Chain
• Unusual Evolving Trend
• Interrupted Time Series
• Related Time Series
• Anomaly Detection

Innovation

• Joint Time Series Chain
• anomaly detection
• time series analysis
• evolving patterns
• robustness to interruptions