A Survey on Change Detection and Time Series Analysis with Applications

With the advent of the digital computer, time series analysis has gained wide attention and is being applied to many fields of science. This paper reviews many traditional and recent techniques for time series analysis and change detection, including spectral and wavelet analyses with their advantages and weaknesses. First, Fourier and least-squares-based spectral analysis methods and spectral leakage attenuation methods are reviewed. Second, several time-frequency decomposition methods are described in detail. Third, several change or breakpoints detection methods are briefly reviewed. Finally, some of the applications of the methods in various fields, such as geodesy, geophysics, remote sensing, astronomy, hydrology, finance, and medicine, are listed in a table. The main focus of this paper is reviewing the most recent methods for analyzing non-stationary time series that may not be sampled at equally spaced time intervals without the need for any interpolation prior to the analysis. Understanding the methods presented herein is worthwhile to further develop and apply them for unraveling our universe.

Automated summary

This paper reviews traditional and recent techniques for time series analysis and change detection, including spectral and wavelet analyses. It describes methods like Fourier analysis, time-frequency decomposition, and breakpoints detection, and lists applications in various fields. The main focus is on analyzing non-stationary time series without interpolation, aiming to further develop and apply these methods for unraveling the universe.