Estimation & Spectral Analysis in Time Series

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Related lectures (31)

General Linear Processes: Wold Decomposition Theorem

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

Signal Processing: Basics and Spectral Analysis

Covers the basics of signal processing, linear estimation, and digital filters.

Time Series: Parametric Estimation

Covers parametric estimation, seasonal modeling, Box-Jenkins methods, variance calculations, and dependence measures in time series analysis.

Time Series: Estimation and Spectral Representation

Explores time series estimation, spectral representation, and p-variate analysis in depth.

Parametric Signal Models: Matlab Practice

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

Power Spectral Density Computation

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

Signals and Systems I: Fourier Transform and Spectral Analysis

Explores Fourier series, energy calculation, functional spaces, correlation spectra, and spectral density in signals and systems.

Linear Estimation & Prediction: Models & Methods

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

Vector Autoregression: Modeling Vector-Valued Time Series

Explores Vector Autoregression for modeling vector-valued time series, covering stability, reverse characteristic polynomials, Yule-Walker equations, and autocorrelations.

Harmonic Signals and Spectrum Estimation

Explores harmonic signals, spectrum estimation, and signal analysis methods using MATLAB tools.

Time Series: Spectral Estimation & Yule Walker

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

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