Abstract
This chapter covers all aspects of time series analysis. In this latest edition, the material has been reorganized so that it now starts with basic concepts regarding stochastic processes and introduces correlation functions as the first step toward defining spectral analysis. We examine the application of spectral analysis to stochastic series and discrete series, and provide a thorough treatment of traditional spectral methods. The chi-squared property of spectral estimates is introduced and discussed as a means of evaluating the statistical significance of spectral estimates. Spectral methods for vector series are briefly discussed, as are rotary spectral estimation methods. The effects of temporal or spatial sampling on the resultant spectral estimates are explored and the resultant aliasing is introduced and discussed, as are the implications for frequency resolution.
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