Prerequisites: Graduate School of Business Student and ISSCM 491 or ISSCM 402N.
Techniques of forecasting and model building are introduced. Methods covered are simple and multiple regression, introduction to time series components, exponential smoothing algorithms, and AIRMA models - Box Jenkins techniques. Business cases are demonstrated and solved using the computer.
Outcomes: Students will be able to forecast business and economic variables to enhance business decisions; Students will learn how AI enhances forecasting techniques by uncovering complex patterns, automating model optimization, and improving forecast accuracy; Students will be equipped with the skills to create advanced, AI-augmented forecasting models, making them invaluable assets in data-driven decision-making environments.
Techniques of forecasting and model building are introduced. Methods covered are simple and multiple regression, introduction to time series components, exponential smoothing algorithms, and AIRMA models - Box Jenkins techniques. Business cases are demonstrated and solved using the computer.
Outcomes: Students will be able to forecast business and economic variables to enhance business decisions; Students will learn how AI enhances forecasting techniques by uncovering complex patterns, automating model optimization, and improving forecast accuracy; Students will be equipped with the skills to create advanced, AI-augmented forecasting models, making them invaluable assets in data-driven decision-making environments.