Demand Forecasting Using Time Series


This course, the second in a specialization for Machine Learning for Supply Chain Fundamentals, delves into all aspects of time series for demand prediction. Students will gain a comprehensive understanding of time series concepts, correlation methods, and demand prediction techniques using autoregressive models. The course culminates in a practical project where learners will apply ARIMA models in Python to forecast demand.

Key learning points include:

  • Understanding time series patterns and exploring exploratory analysis with time series
  • Examining independence and autocorrelation in time series
  • Learning about regression, autoregressive, and ARIMA models for demand prediction
  • Engaging in a final project to apply ARIMA models in Python for demand forecasting

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Demand Forecasting Using Time Series
Course Modules

This course is divided into four modules covering fundamental to advanced concepts of time series analysis, correlation, and demand prediction using autoregressive models and ARIMA models in Python.

A First Glance at 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.

Independence and Autocorrelation

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.

Regression and ARIMA Models

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.

Final Project

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.

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