Time Series Models: Autoregressive Processes

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Parametric Signal Models: Matlab Practice

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

Univariate time series: Analysis & Modeling

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

Time Series: Autoregressive Models

Explores autoregressive models for time series analysis, covering AR(1), AR(2), identification, and MA models.

Integrated and Seasonal Processes: Time Series

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

Time Series: Parametric Estimation

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

Multivariate Time Series: Cointegration & Forecasting

Explores multivariate time series analysis, cointegration, forecasting with ARMA models, and practical applications in interest rates analysis.

Time Series: Common Models

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

Univariate Time Series Analysis

Explores univariate time series analysis, covering stationarity, ARMA processes, model selection, and unit root tests.

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.

Count Data Models & Univariate Time Series Analysis

Covers count data models and Poisson regression, then transitions to univariate time series analysis for forecasting economic variables.