The approach presented here extends the modeling of M-dimensional (spatial) time series from the time domain into the frequency domain. The autocovariance function for an M-dimensional time series is transformed to obtain the power spectrum in M dimensions. The latter describes the variance within the series and can be used to identify dependencies and/or test the adequacy of a fitted model. An example is provided.
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