Sequential Change Point Detection in Dynamic Non-Stationary Manufacturing Processes

Sequential monitoring of multivariate time series to detect sudden changes in the data-generating process is a fundamental problem in statistics and signal processing. Most existing detection algorithms assume (a) no cross-correlations between time series components and (b) stationarity with fixed parameters between consecutive change points. These assumptions are often violated in real-world applications, such as manufacturing processes, leading to overfitting or inaccurate change point identification. To address this limitation, we introduce a general modeling framework that incorporates local dynamics and cross-correlations in multivariate time series and propose a novel sequential detection algorithm, dscpd (dynamic sequential change point detection). The method detects abrupt shifts in the mean while accounting for local dynamics through a multivariate random walk model and cross-correlations through a vector autoregressive process. In addition, dscpd estimates shift sizes and constructs confidence intervals, facilitating root cause analysis of sudden changes. We establish theoretical properties under mild conditions, including false-positive rate control, detection power calculations, and localization error bounds. Simulation studies, comparisons with existing methods, and applications to real-world data sets such as paper production and semiconductor manufacturing demonstrate the effectiveness of dscpd for detecting abrupt changes in complex multivariate time series. History: Eunshin Byon served as the senior editor for this article. Funding: Safikhani was supported in part by the National Institute on Aging of the National Institutes of Health under Award No. RF1AG090462. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Data Ethics & Reproducibility Note: No data ethics considerations are foreseen related to this paper. The code capsule is available in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2025.0068 ).