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[2602.13649] Joint Time Series Chain: Detecting Unusual Evolving Trend across Time Series
Summary
The paper introduces the Joint Time Series Chain (JTSC) concept, enhancing the detection of unusual evolving trends across interrupted or related time series, improving upon existing methods.
Why It Matters
This research addresses limitations in current time series analysis methods by introducing a new framework that captures unexpected patterns across multiple time series, which is crucial for fields like manufacturing and finance where timely insights can lead to significant operational improvements.
Key Takeaways
- The Joint Time Series Chain (JTSC) improves detection of unusual trends across multiple time series.
- Existing methods often overlook evolving patterns in interrupted time series.
- The proposed ranking criterion effectively identifies the best chains for analysis.
- Empirical evaluations demonstrate JTSC's superiority over traditional methods.
- The framework has practical applications, as shown in a case study with Intel.
Computer Science > Machine Learning arXiv:2602.13649 (cs) [Submitted on 14 Feb 2026] Title:Joint Time Series Chain: Detecting Unusual Evolving Trend across Time Series Authors:Li Zhang, Nital Patel, Xiuqi Li, Jessica Lin View a PDF of the paper titled Joint Time Series Chain: Detecting Unusual Evolving Trend across Time Series, by Li Zhang and 3 other authors View PDF HTML (experimental) 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 se...
