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An application of spectral analysis to the study of time series using mathematical models known as covariance stationary stochastic processes, which are useful representations of autocorrelated time series. A discussion of the rationale, backgrounds, and basic ideas of the study is given. Three simulated experiments are presented as examples of how to apply spectral analysis. An application of spectral analysis to the study of time series using mathematical models known as covariance stationary stochastic processes, which are useful representations of autocorrelated time series. A discussion of the rationale, backgrounds, and basic ideas of the study is given. Three simulated experiments are presented as examples of how to apply spectral analysis. (See also RM-3789-PR.)

This report is part of the RAND Corporation research memorandum series. The Research Memorandum was a product of the RAND Corporation from 1948 to 1973 that represented working papers meant to report current results of RAND research to appropriate audiences.

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