Chapter 1 - Spectrum Parameter Estimation in Time Series Analysis†

Publisher Summary In most fields of science and engineering, time series occur often, that is, series of observations that depend on a discrete or continuous time parameter and fluctuate in a disorderly fashion in time. Series of this kind cannot be reasonably described deterministically and should be studied through statistical methods only. Parametric spectral estimates based on mixed autoregressive-moving average models are not as popular as autoregressive estimates, but they also have been used in a dozen works. Estimation through some parametric model fitting might be studied in this case, along with more usual nonparametric spectral estimation. After the parameters of the model have been evaluated, it is advisable to employ some method of diagnostic checking to examine the agreement between the model and the available data, that is, to apply some goodness-of-fit tests.

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