Walsh Fourier Transform of Locally Stationary Time Series
Publication in refereed journal

香港中文大學研究人員

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替代計量分析
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其它資訊
摘要A 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.
著者Huang Zhelin, Chan Ngai Hang
期刊名稱Journal of Time Series Analysis
出版年份2020
月份3
卷號41
期次2
出版社Wiley
出版地London
頁次312 - 340
國際標準期刊號0143-9782
電子國際標準期刊號1467-9892
語言美式英語
關鍵詞Classification method, dyadic stationary process, locally dyadic stationary processes, Walsh–Fourier transform

上次更新時間 2021-15-01 於 23:25