Forecasting with Regression Analysis

Many forecasting applications and decision making processes require explanatory variables, making regression analysis an attractive solution in the business world. This course offers a holistic view of a regression-based approach to forecasting. We will discuss several representative regression techniques for forecasting both continuous variables and categorical variables, with the output in both point and probabilistic forms. We will also discuss several important concepts such as model selection and regularization. Real-world data and examples will be used to illustrate the theory. The course also includes about two hours of lab session for the attendees to practice the regression techniques in SAS.

Course Outline

Forecasting problems

Error analysis

Micro and macro views of model selection

Linear regression

Model selection methods

Lab session I

Ridge regression and lasso

Support vector regression

Robust regression

Quantile regression

Fuzzy regression

Logistic regression

Lab session II