Lectures in course MATH-342: Time series

Covers parametric estimation in time series analysis, including integrated processes and seasonal modeling.

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

Spectral Analysis: Time Series

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

Estimation & Spectral Analysis in Time Series

Covers the estimation of time series models and spectral analysis in depth.

Spectral Analysis: Integrated Spectrum and Autocovariance

Explores spectral analysis, integrated spectrum, autocovariance, estimation, and convergence in time series models.

Linear Time Invariant Filters: Time Series

Explores estimation, ergodicity, sampling, and linear time invariant filters in time series analysis.

Time Series: Estimation and Spectral Representation

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

Multivariate Time Series and Spectral Representation

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

Time Series: Spectral Analysis and Linear Filtering

Explores spectral analysis, aliasing, and linear filtering in time series data.

Spectral Estimation: Periodogram and Tapering

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

Spectral & Parametric Estimation: Time Series

Covers spectral estimation techniques like tapering and parametric estimation, emphasizing the importance of AR models and Whittle likelihood in time series analysis.

Time Series: Linear Filtering and Spectral Estimation

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

Time Series: Representation and Modelling

Covers the stochastic properties of time series, stationarity, autocovariance, special stochastic processes, spectral density, digital filters, estimation techniques, model checking, forecasting, and advanced models.

Time Series: Multi-Tapering and Parametric Estimation

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

Integrated and Seasonal Processes: Time Series

Explores parametric estimation, integrated processes, seasonal modeling, and ARIMA model building in time series analysis.

Spectral Estimation in Time Series

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

Box-Jenkins Methodology: Building Time Series Models

Covers the Box-Jenkins methodology for building time series models, including model identification, variance calculations, and model diagnostics.

Time Series: Spectral Estimation & Yule Walker

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

Time Series Analysis: ARIMA and Seasonal Models

Covers ARIMA, ARI, and seasonal models for time series analysis.

Time Series: Parametric Estimation

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