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Covers model selection, diagnostics, and forecasting in time series analysis, emphasizing the challenges of determining the model order based on autocorrelation and partial autocorrelation functions.
Covers the modeling of fluid instabilities with linear perturbation theory and explores the origin of unpredictability in turbulence through the Navier-Stokes equations.
Covers the stochastic properties of time series, stationarity, autocovariance, special stochastic processes, spectral density, digital filters, estimation techniques, model checking, forecasting, and advanced models.
