Detection of a Sparse Change in High-Dimensional Time Series

2025/07/29 by Huang, Jingyan
#FOS: Computer and information sciences #Methodology (stat.ME)

paper · doi:10.48550/arxiv.2507.21442

Abstract

Consider the detection of a sparse change in high-dimensional time-series. We introduce Sparsity Likelihood-based (SL-based) score and the change-points detection procedure in multivariate normal model with general covariance structure. SL-based algorithm is proved to achieve that supremum of error probabilities converges to 0. We run the simulation studies for SL-based algorithm and also illustrate its applications to a S&P500 dataset.

Citations

• Optimal multiple change-point detection for high-dimensional data
• High-dimensional changepoint estimation via sparse projection
• Optimal sequential detection in multi-stream data
• Multiscale Change-Point Inference

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• Optimal multiple change-point detection for high-dimensional data
• Covariance scanning for adaptively optimal change point detection in high-dimensional linear models
• High-Dimensional Change Point Analysis for Temporally Dependent Data
• Spectral Change Point Estimation for High Dimensional Time Series by Sparse Tensor Decomposition