Forecasting Using Machine-Learning Approaches

Catalog description

Credits 3. 3 Lecture Hours. Forecasting principles and methods, including point and interval forecasts; accuracy; statistical methods in the context of forecasting, including exponential smoothing and Auto Regressive Integrated Moving Average (ARIMA), exogenous variables, seasonality and trends; tree-based models for predictions, prophet models, probabilistic forecasts and predictive and prescriptive analytics. Prerequisites: Grade of C or better in DAEN 321 ; junior or senior classification.