ADEC 7406 Predictive Analytics/Forecasting
Econometrics This course will expose students to the most popular forecasting techniques used in industry. We will cover time series data manipulation and feature creation, including working with transactional and hierarchical time series data as well as methods of evaluating forecasting models. We will cover basic univariate Smoothing and Decomposition methods of forecasting including Moving Averages, ARIMA, Holt-Winters, Unobserved Components Models and various filtering methods (Hodrick-Prescott, Kalman Filter). Time permitting, we will also extend our models to multivariate modeling options such as Vector Autoregressive Models (VAR). We will also discuss forecasting with hierarchical data and the unique challenges that hierarchical reconciliation creates. The course will use the R programming language though no prior experience with R is required.
Course overview
- Department
- Advancing Studies
- School
- ADV
- Credits
- 3
- Level
- Graduate
- Offered
- Periodically in the Fall,Periodically in the Spring,Periodically in the Summer
Catalog details
- Prerequisites
- ADEC7301/ADAN7301 or ADEC7310/ADAN7309 or ADAN7310/ADEC7309
Requirements fulfilled
No source-backed degree requirement is attached to this course yet.
Official evaluation summary
Data freshness
Instructors
- Arvind Sharma
- Lawrence Fulton
- Robert Bradley
Sections
No current section details are available.