Applied Time Series Analysis

3 Credits · Level: Undergraduate

This course introduces students to the theory and application of time series methods for data that are collected over time. Topics include exploratory data analysis tools, methods for detrending, and seasonal adjustment of data, regression methods including regression with autoregressive errors, smoothing techniques including exponential smoothing, modeling and forecasting based on the ARIMA class of models, and ARCH/GARCH models. Time permitting, advanced topics including neural network methods for time series, and methods for analyzing multivariate time series. Students gain hands-on experience of applied time series methods for real data sets using the statistical software, R. Examples will be drawn from a variety of disciplines including business, finance, economics, health, environmental studies, and ecology. Students are required to complete a final data analysis project in which they apply the knowledge they gained in the course to real data.