TIME SERIES ANALYSIS AND FORECASTING

Catalog description

Time Series Analysis and Forecasting provides a hands-on introduction to forecasting methods and predictive analytics with a focus on real -world business applications. Students will learn to develop, evaluate, and apply forecasting models using computer software, covering techniques that may include autoregression, moving averages, exponential smoothing, time series decomposition, regression -based models, ARIMA (Box -Jenkins) models, causal forecasting , and, optionally, advanced models of mean and variance . The course also introduces predictive analytics methods such as classification, clustering, ensemble models, and text mining. Emphasis is placed on understanding the forecasting process, selecting appropriate models, and implementing solutions to support d ata-driven decision -making across business contexts. Prerequisite: STAT 230 with a minimum of B- or MATH 231.