A wavelet-based approach for imputation in nonstationary multivariate time series

Wilson, Rebecca and Eckley, Idris and Nunes, Matthew and Park, Timothy Alexander (2021) A wavelet-based approach for imputation in nonstationary multivariate time series. Statistics and Computing, 31. ISSN 0960-3174

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Abstract

Many multivariate time series observed in practice are second order nonstationary, i.e. their covariance properties vary over time. In addition, missing observations in such data are encountered in many applications of interest, due to recording failures or sensor dropout, hindering successful analysis. This article introduces a novel method for data imputation in multivariate nonstationary time series, based on the so-called locally stationary wavelet modelling paradigm. Our methodology is shown to perform well across a range of simulation scenarios, with a variety of missingness structures, as well as being competitive in the stationary time series setting. We also demonstrate our technique on data arising in a health monitoring application.

Item Type:
Journal Article
Journal or Publication Title:
Statistics and Computing
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
151873
Deposited By:
Deposited On:
18 Feb 2021 11:50
Refereed?:
Yes
Published?:
Published
Last Modified:
04 Mar 2021 10:25