Practical Time Series Analysis

This module covers the basics of R programming and reviews fundamental concepts in inferential and descriptive statistics needed for the course.

This module introduces time series visualization and basic modeling concepts. Topics include time plots, stationarity, autocovariance function, autocorrelation function (ACF), random walk, and moving average processes.

This module covers important concepts in time series analysis including stationarity, backward shift operator, invertibility, and duality. It also introduces autoregressive processes and Yule-Walker equations.

This module introduces partial autocorrelation and the PACF. It delves deeper into Yule-Walker equations and applies the concepts to real-world datasets. The module includes simulations and model fitting exercises.

This module introduces the Akaike Information Criterion for model selection, and covers mixed models such as ARMA and ARIMA. It also includes the Q-statistic and applies these concepts to real-world datasets.

In this final module, SARIMA models are introduced and applied to various datasets. The module also covers forecasting techniques including simple, double, and triple exponential smoothing.