Nonparametric change point detection in multivariate piecewise stationary time series

Raanju R. Sundararajan · Mohsen Pourahmadi

15 citations0/0 files storedJournal of nonparametric statistics

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Advanced Statistical Process Monitoring Statistical Methods and Inference Time Series Analysis and Forecasting

Abstract

Detecting change points in multivariate time series is an important problem with numerous applications. We develop a nonparametric method to detect multiple change points in multivariate piecewise stationary processes when the locations and number of change points are unknown. Based on a test statistic that measures differences in the spectral density matrices through the norm, we sequentially identify points of local maxima in the test statistic and test for the significance of each of them being change points. In addition, the components responsible for the change in the covariance structure at each detected change point are identified. The asymptotic properties of the test for significant change points under the null and alternative hypothesis are derived. We illustrate the better performance of our method in comparison to some of the recent methods through a few simulation examples and discuss applications of our method in seismology and finance.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 15 citations exist upstream.

References

1995Journal of the American Statistical Association 1994Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance 2013A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data 1975On Tests for Detecting Change in Mean 1997Testing and Locating Variance Changepoints with Application to Stock Prices 2009Break detection in the covariance structure of multivariate time series models 2009Break detection in the covariance structure of multivariate time series models 2017High Dimensional Change Point Estimation via Sparse Projection

Cited by

2022Break point detection for functional covariance

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.