Walsh Fourier Transform of Locally Stationary Time Series
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AbstractA new time-frequency model and a method to classify time series data are proposed in this article. By viewing the observed signals as realizations of locally dyadic stationary (LDS) processes, a LDS model can be used to provide a time-frequency decomposition of the signals, under which the evolutionary Walsh spectrum and related statistics can be defined and estimated. The classification procedure is as follows. First choose a training data set that comprises two groups of time series with a known group. Then compute the time frequency feature (the energy) using the training data set, and use a best tree method to maximize the discrepancy of this feature between the two groups. Finally, choose the testing data set with the unknown group as validation data, and use a discriminant statistic to classify the validation data to one of the groups. The classification method is illustrated via an electroencephalographic dataset and the Ericsson B transaction time dataset. The proposed classification method performs better for integer-valued time series in terms of classification error rates in both simulations and real-life applications.
All Author(s) ListHuang Zhelin, Chan Ngai Hang
Journal nameJournal of Time Series Analysis
Year2020
Month3
Volume Number41
Issue Number2
PublisherWiley
Place of PublicationLondon
Pages312 - 340
ISSN0143-9782
eISSN1467-9892
LanguagesEnglish-United States
KeywordsClassification method, dyadic stationary process, locally dyadic stationary processes, Walsh–Fourier transform

Last updated on 2020-29-11 at 23:44