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Expectile periodogram
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DOI：10.1016/j.csda.2025.108337.png)
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Abstract
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This paper introduces a novel periodogram-like function, called the expectile periodogram (EP), for modeling spectral features of time series and detecting hidden periodicities. The EP is constructed from trigonometric expectile regression (ER), in which a specially designed loss function is used to substitute the squared l(2) norm that leads to the ordinary periodogram. The EP retains the key properties of the ordinary periodogram as a frequency-domain representation of serial dependence in time series, while offering a more comprehensive understanding by examining the data across the entire range of expectile levels. The asymptotic theory is established to investigate the relationship between the EP and the so-called expectile spectrum. Simulations demonstrate the efficiency of the EP in the presence of hidden periodicities. In addition, by leveraging the inherent two-dimensional nature of the EP, we train a deep learning model to classify earthquake waveform data. Notably, our approach outperforms alternative periodogram-based methods in terms of classification accuracy.
Keywords:
Expectile regression
Hidden periodicity
Periodogram
Spectral analysis
Time series analysis
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1.6
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45
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