This SCMT 3623 course module provides a focused introduction to Time Series Forecasting and its application in supply chain management and demand planning. It is ideal for students and professionals seeking quantitative forecasting skills.
The course begins with an introduction to time series forecasting, explaining key components such as trend, seasonality, cyclic patterns, and randomness. Learners understand how historical demand data is analyzed to predict future demand patterns.
Next, the module covers forecast accuracy measurement and time series regression. Participants learn how to evaluate forecast performance using common accuracy metrics such as MAD (Mean Absolute Deviation), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error). Regression analysis is introduced to model relationships betwee
