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

TL;DR

This paper introduces the Joint Time Series Chain, a novel method for detecting unexpected evolving trends across interrupted or related time series, outperforming existing approaches and demonstrated through empirical and real-world manufacturing applications.

Contribution

It proposes a new definition of time series chains tailored for cross-series and interrupted data, addressing robustness issues and improving pattern detection.

Findings

Outperforms existing TSC methods in locating unusual patterns

Effective in handling interrupted and related time series

Validated with real-life manufacturing data from Intel

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 .