Multivariate Time Series and Spectral Representation

Related lectures (31)

Page 3 of 4

Covers Vector Autoregression (VAR) in time series analysis, including sampling properties and examples of VAR processes.

Covers Multi-Tapering and Parametric Estimation in Time Series analysis, including spectral estimation and AR model fitting.

Covers the computation of power spectral density and the design of communication systems.

Explores the fundamentals of time series analysis, including stationarity, linear processes, forecasting, and practical aspects.

Covers parametric signal models and practical Matlab applications for Markov chains and AutoRegressive processes.

Explores narrow and spread spectrum communications, harmonic signals, and spectral estimation methods using MATLAB.

Explores linear estimation and prediction in AR parametric models, focusing on Yule Walker equations and Wiener filter.

Explores long memory in time series and Autoregressive Conditional Heteroskedasticity processes in financial data.

On Time Series explores Spectral Estimation, Yule Walker method, and ARIMA models.

Explores general linear processes, the Wold decomposition theorem, and spectral analysis in time series analysis.