DSI-5100 Forecasting Analytics

In this course students will learn how to choose an appropriate time series forecasting method, fit the model, evaluate its performance, and use it for forecasting. The course will focus on the most popular business forecasting methods: regression models, smoothing methods including moving average (MA) and exponential smoothing, and autoregressive (AR) models. It will also discuss enhancements such as second-layer models and ensembles, and various issues encountered in practice. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.

Credits: 3

Offered in Semester, Jul 2024, Nov 2024, Mar 2025

(Please visit the University bookstore to view the correct materials for each course by semester as the contents of the actual online syllabus may differ from the preview due to updates or revisions)