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Classification of non‐stationary time series
Author(s) -
Krzemieniewska Karolina,
Eckley Idris A.,
Fearnhead Paul
Publication year - 2014
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.51
Subject(s) - series (stratigraphy) , class (philosophy) , stationary process , computer science , variation (astronomy) , artificial intelligence , wavelet , pattern recognition (psychology) , yield (engineering) , state (computer science) , machine learning , mathematics , algorithm , geology , physics , paleontology , astrophysics , thermodynamics
In this paper we consider the problem of classifying non‐stationary time series. The method that we introduce is based on the locally stationary wavelet paradigm and seeks to take account of the fact that there may be within‐class variation in the signals being analysed. Specifically, we seek to identify the most stable spectral coefficients within each training group and use these to classify a new, previously unseen, time series. In both simulated examples and an aerosol spray example provided by an industrial collaborator, our approach is found to yield superior classification performance when compared against the current state of the art. Copyright © 2014 John Wiley & Sons, Ltd.