Data-driven learning of the number of states in multi-state autoregressive models

Jie Ding, Mohammad Noshad, Vahid Tarokh

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

5 Scopus citations

Abstract

In this work, we consider the class of multi-state autoregressive processes that can be used to model non-stationary time series of interest. In order to capture different autoregressive (AR) states underlying an observed time series, it is crucial to select the appropriate number of states. We propose a new and intuitive model selection technique based on the Gap statistics, which uses a null reference distribution on the stable AR filters to identify whether adding a new AR state significantly improves the performance of the model. To that end, we define a new distance measure between two AR filters based on the mean squared prediction error, and propose an efficient method to generate stable filters that are uniformly distributed in the coefficient space. Numerical results are provided to evaluate the performance of the proposed approach.

Original languageEnglish (US)
Title of host publication2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages418-425
Number of pages8
ISBN (Electronic)9781509018239
DOIs
StatePublished - Apr 4 2016
Externally publishedYes
Event53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015 - Monticello, United States
Duration: Sep 29 2015Oct 2 2015

Publication series

Name2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015

Other

Other53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015
Country/TerritoryUnited States
CityMonticello
Period9/29/1510/2/15

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