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Activity Number:
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457
- Novel Statistical Approaches to Time Series of Networks
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #304407
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Title:
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Quantile-Frequency Analysis and Functional Principal Components for Discriminant Analysis of Time Series
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Author(s):
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Ta-Hsin Li*
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Companies:
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IBM T. J. Watson Research Center
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Keywords:
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quantile periodogram;
spectral analysis;
time series;
classification;
functional data analysis;
principal component
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Abstract:
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Quantile periodogram is a recently proposed tool for spectral analysis of time-series data. Derived from trigonometric quantile regression, the quantile periodogram offers a capability of quantile-frequency analysis (QFA) that characterizes the oscillatory behavior of time series around different quantile levels. This talk introduces a QFA-based functional principal component analysis (FPCA) method that extracts useful features for discriminant analysis of time series. A real-world dataset of nondestructive evaluation (NDE) of mechanical systems is used to demonstrate the advantages of the proposed method over the traditional spectral analysis method based on the ordinary periodogram.
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Authors who are presenting talks have a * after their name.
