A Semi-Parametric Estimation Method for the Quantile Spectrum with an Application to Earthquake Classification Using Convolutional Neural Network
KAUST DepartmentStatistics Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Preprint Posting Date2019-10-16
Online Publication Date2020-08-29
Print Publication Date2021-02
Permanent link to this recordhttp://hdl.handle.net/10754/660677
MetadataShow full item record
AbstractIn this paper, a new estimation method is introduced for the quantile spectrum, which uses a parametric form of the autoregressive (AR) spectrum coupled with nonparametric smoothing. The method begins with quantile periodograms which are constructed by trigonometric quantile regression at different quantile levels, to represent the serial dependence of time series at various quantiles. At each quantile level, we approximate the quantile spectrum by a function in the form of an ordinary AR spectrum. In this model, we first compute what we call the quantile autocovariance function (QACF) by the inverse Fourier transformation of the quantile periodogram at each quantile level. Then, we solve the Yule-Walker equations formed by the QACF to obtain the quantile partial autocorrelation function (QPACF) and the scale parameter. Finally, we smooth QPACF and the scale parameter across the quantile levels using a nonparametric smoother, convert the smoothed QPACF to AR coefficients, and obtain the AR spectral density function. Numerical results show that the proposed method outperforms other conventional smoothing techniques. We take advantage of the two-dimensional property of the estimators and train a convolutional neural network (CNN) to classify smoothed quantile periodogram of earthquake data and achieve a higher accuracy than a similar classifier using ordinary periodograms.
CitationChen, T., Sun, Y., & Li, T.-H. (2021). A semi-parametric estimation method for the quantile spectrum with an application to earthquake classification using convolutional neural network. Computational Statistics & Data Analysis, 154, 107069. doi:10.1016/j.csda.2020.107069
SponsorsThis research was supported by funding from King Abdullah University of Science and Technology (KAUST). We would like to thank the editor, associate editor, and reviewers for their valuable comments.