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Accession Number:
AD0612281
Title:
SPECTRAL ANALYSIS OF TIME SERIES GENERATED BY SIMULATION MODELS,
Corporate Author:
RAND CORP SANTA MONICA CALIF
Report Date:
1965-02-01
Abstract:
This study applies spectral analysis to the study of time series generated by simulated stochastic models. As these data are autocorrelated, analysis by methods applicable to independent observations is precluded. Mathematical models known as covariance stationary stochastic processes are useful representations of autocorrelated time series. The increased publication of literature describing stochastic processes and spectral analysis, in particular, is making these ideas available to an increasing audience. Section I presents a rationale for our interest in time series models and spectral analysis. Section II describes the basic notions of covariance stationary processes. It emphasizes the equivalence of looking at these processes in both the time and frequency domains the compactness of frequency domain analysis seemingly recommends it over correlation analysis. Section III provides a heuristic background for understanding statistical spectral analysis. Simple frequency-domain statistical properties are emphasized and compared with the rather involved sampling properties of estimated correlograms. Several relevant statistical tests are described. Three simulated experiments are used as examples of how to apply spectral analysis. These are described in Sec. IV.
Pages:
0079
Contract Number:
AF49 638 700
File Size:
0.00MB
