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Covers the stochastic properties of time series, stationarity, autocovariance, special stochastic processes, spectral density, digital filters, estimation techniques, model checking, forecasting, and advanced models.
Covers ARMA models for time series forecasting, discussing implications, properties of forecast error, challenges with predictions, and covariance models.
Explores spatial regression models, addressing spatial autocorrelation challenges and the concept of spatial lag models to correct biases and improve inference accuracy.
