Spectral analysis and stationarity tests

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Time Series: Linear Filtering and Spectral Estimation

Explores linear filtering, spectral estimation, and second-order stationarity in time series analysis.

Time Series: Stochastic Properties and Modelling

Explores the stochastic properties and modelling of time series, covering autocovariance, stationarity, spectral density, estimation, forecasting, ARCH models, and multivariate modelling.

Signal Representations

Covers signal representations using concepts such as Haar wavelets and FIR filters.

Spectral Estimation: Periodogram and Tapering

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

Time Series: Estimation and Spectral Representation

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

General Linear Processes: Wold Decomposition Theorem

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

Signal Representations

Covers the concept of signal representations using wavelets and the Haar wavelet.

Spectral Estimation in Time Series

Covers spectral estimation in time series analysis, including imaging kernels, tapering methods, and AR models.

Causal Systems & Transforms: Delay Operator Interpretation

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.

Wavelet Design: Fourier Transform

Covers the design of wavelets using Fourier transform and procedures for selecting wavelet functions.