Statistics and Its Interface

Volume 16 (2023)

Number 1

Special issue on recent developments in complex time series analysis – Part I

Guest editors: Robert T. Krafty (Emory Univ.), Guodong Li (Univ. of Hong Kong), Anatoly Zhigljavsky (Cardiff Univ.)

AutoSpec: detection of narrowband frequency changes in time series

Pages: 97 – 108

DOI: https://dx.doi.org/10.4310/21-SII703

Author

David S. Stoffer (Department of Statistics, University of Pittsburgh, Pennsylvania, U.S.A.)

Abstract

Most established techniques that search for structural breaks in time series have a difficult time identifying small changes in the process, especially when looking for narrowband frequency changes. The problem is that many of the techniques assume very smooth local spectra and tend to produce overly smooth estimates. The problem of oversmoothing tends to produce spectral estimates that miss slight frequency changes because frequencies that are close together will be lumped into one frequency. The goal of this work is to develop techniques that concentrate on detecting slight frequency changes by requiring a high degree of resolution in the frequency domain.

Keywords

spectral analysis, structural breaks, Whittle likelihood, minimum description length

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Received 25 January 2021

Accepted 1 September 2021

Published 28 December 2022