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