Demand Forecasting Using Time Series

This module provides a comprehensive introduction to time series, covering basic concepts, exploratory analysis, and time series patterns. Students will gain foundational knowledge of time series analysis and its relevance in machine learning for supply chain fundamentals.

Module 2 explores independence and autocorrelation in time series, focusing on correlation methods, shifting time series, and understanding autocorrelation patterns. Learners will gain practical skills in identifying and analyzing autocorrelation in time series data.

In Module 3, students delve into regression and ARIMA models, learning about lagged regression, autoregressive models, and ARIMA models for demand prediction. The module includes a programming assignment to apply ARIMA models in Python for demand forecasting.

The final module involves a practical project, applying the knowledge and skills acquired in the previous modules to predict demand using ARIMA models in Python. Students will gain hands-on experience in forecasting demand and analyzing the results.