Detecting structural changes in dependent data

Jie DIng, Yu Xiang, Lu Shen, Vahid Tarokh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the era of big data, a frequently encountered task is to model and identify structural changes in the data generating process. It is quite challenging especially when data are dependent and massive, requiring computationally efficient analysis. To address the challenge, we model the data generating process as a segment-wise autoregression, and propose a multi-window method that is both effective and efficient for discovering the structural changes. The proposed approach was motivated by transforming a segment-wise autoregression into a multivariate time series that is asymptotically segment-wise independent and identically distributed. We then derive theoretical guarantees for (almost surely) selecting the true number of change points of segment-wise independent multivariate time series. In particular, we prove that a wide variety of penalized selection procedure produces a strongly consistent selection of the optimal number of change points, under mild assumptions. We demonstrate the theory and strength of the proposed algorithms by experiments on both synthetic and real-world data.

Original languageEnglish (US)
Title of host publication2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages750-754
Number of pages5
ISBN (Electronic)9781509059904
DOIs
StatePublished - Mar 7 2018
Event5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Montreal, Canada
Duration: Nov 14 2017Nov 16 2017

Publication series

Name2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
Volume2018-January

Other

Other5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017
CountryCanada
CityMontreal
Period11/14/1711/16/17

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Keywords

  • Autoregression
  • Information criteria
  • Strong Consistency
  • Time Series

Cite this

DIng, J., Xiang, Y., Shen, L., & Tarokh, V. (2018). Detecting structural changes in dependent data. In 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings (pp. 750-754). (2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2017.8309060