Type of Document	Dissertation
Author	Liang, Hong
Author's Email Address	holiang@vt.edu
URN	etd-05152002-152728
Title	Adaptive Fourier Analysis For Unequally-Spaced Time Series Data
Degree	PhD
Department	Statistics
Advisory Committee	Advisor Name Title Foutz, Robert Committee Chair Anderson-Cook, Christine M. Committee Member Jensen, Donald R. Committee Member Reynolds, Marion R. Jr. Committee Member Terrell, George R. Committee Member
Keywords	Adaptive Fourier Analysis Unequally-Spaced Time Series Wavelet Walsh-Fourier
Date of Defense	2002-04-16
Availability	unrestricted
Abstract Adaptive Fourier Analysis For Unequally-Spaced Time Series Data by Hong Liang Robert V. Foutz, Chairman Statistics (ABSTRACT) Fourier analysis, Walsh-Fourier analysis, and wavelet analysis have often been used in time series analysis. Fourier analysis can be used to detect periodic components that have sinusoidal shape; however, it might be misleading when the periodic components are not sinusoidal. Walsh-Fourier analysis is suitable for revealing the rectangular trends of time series. The flaw of the Walsh-Fourier analysis is that Walsh functions are not periodic. The resulting Walsh-Fourier analysis is more difficult to interpret than classical Fourier analysis. Wavelet analysis is very useful in analyzing and describing time series with gradual frequency changes. Wavelet analysis also has a shortcoming by giving no exact meaning to the concept of frequency because wavelets are not periodic functions. In addi-tion, all three analysis methods above require equally-spaced time series observations. In this dissertation, by using a sequence of periodic step functions, a new analysis method, adaptive Fourier analysis, and its theory are developed. These can be applied to time series data where patterns may take general periodic shapes that include sinusoids as special cases. Most importantly, the resulting adaptive Fourier analysis does not require equally-spaced time series observations.
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