Autocovariance

Related lectures (31)

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Explores spectral analysis in time series, focusing on spectral density functions and integrated spectra.

Discusses weak-sense stationarity in continuous-time stochastic processes and the calculation of autocorrelation and cross-correlation functions.

Covers common time series models, trend removal, and seasonality adjustment techniques.

Explores forecasting methods and long memory in time series analysis.

Covers stochastic models for communications and the analysis of random processes in communication systems.

Explores multivariate time series analysis, emphasizing spectral representation and estimation methods.

Covers the concept of sphere packing and its significance in time shifts and Nyquist criterion.

Explores spectral representations, ACVS estimation, and spectral estimation in time series analysis.

Covers the analysis and modeling of univariate time series, focusing on stationarity, ARMA processes, and forecasting.

Explores ring polymer molecular dynamics, its accuracy in different limits, and its application in multidimensional systems and liquid environments.