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Demand Forecasting Using Time Series

This course is part of Machine Learning for Supply Chains Specialization

Instructors: Rajvir Dua +1 more

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What you'll learn

Building ARIMA models in Python to make demand predictions

Developing the framework for more advanced neural netowrks (such as LSTMs) by understanding autocorrelation and autoregressive models.

Skills you'll gain

Exploratory Data Analysis

Regression Analysis

Data Visualization

Supply Chain Management

Trend Analysis

Data Analysis

Pandas (Python Package)

Statistical Modeling

Demand Planning

Time Series Analysis and Forecasting

Customer Demand Planning

Matplotlib

Forecasting

Predictive Modeling

There are 4 modules in this course

This course is the second in a specialization for Machine Learning for Supply Chain Fundamentals. In this course, we explore all aspects of time series, especially for demand prediction. We'll start by gaining a foothold in the basic concepts surrounding time series, including stationarity, trend (drift), cyclicality, and seasonality. Then, we'll spend some time analyzing correlation methods in relation to time series (autocorrelation). In the 2nd half of the course, we'll focus on methods for demand prediction using time series, such as autoregressive models. Finally, we'll conclude with a project, predicting demand using ARIMA models in Python.

Independence and Autocorrelation

Regression and ARIMA Models

Final Project

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