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# Forecasting Product Demand in R

Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.

4 hours13 videos50 exercises

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## Course Description

Accurately predicting demand for products allows a company to stay ahead of the market. By knowing what things shape demand, you can drive behaviors around your products better. This course unlocks the process of predicting product demand through the use of R. You will learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example. By the end of the course you will be able to predict demand for multiple products across a region of a state in the US. Then you will roll up these predictions across many different regions of the same state to form a complete hierarchical forecasting system.

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1. 1

### Forecasting Demand With Time Series

Free

When it comes to forecasting, time series modeling is a great place to start! You need to forecast out the future values of sales demand and a good baseline approach would be ARIMA models. In this chapter you'll learn how to quickly implement ARIMA models and get good initial forecasts for future product demand.

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50 xp
Importing data
100 xp
Plotting / visualizing data
100 xp
ARIMA Time Series 101
50 xp
auto.arima() function
100 xp
Interpret auto.arima
50 xp
Forecasting and Evaluating
50 xp
Forecast function
100 xp
Calculating MAPE and MAE
100 xp
Visualizing Forecast
100 xp
Confidence Intervals for Forecast
100 xp
2. 2

### Components of Demand

Economic theory has a lot to say about predicting values of demand. Obviously, external factors like price, seasonality, and timing of promotions will drive some aspects of product demand. In this chapter you'll learn about the basics around price elasticity models and how to incorporate seasonality and promotion timing factors into our product demand forecasts.

3. 3

### Blending Regression With Time Series

Time series models and pricing regressions don't have to be thought of as separate approaches to product demand forecasting. They can be combined! In this chapter you'll learn about two ways of "combining" the information gained in both modeling approaches - transfer functions and forecast ensembling.

4. 4

### Hierarchical Forecasting

Everything up until this point deals with making individual models for forecasting product demand. However, we haven't taken advantage of the fact that all of these products form a product hierarchy of sales. Products make up regions and regions make up states. How can we ensure that our forecasts reconcile correctly up and down the hierarchy? In this chapter you'll learn about hierarchical forecasting and how to use it to your advantage in forecasting product demand.

### GroupTraining 2 or more people?

datasets

Beverage producer sales

collaborators

Aric LaBarr

Director and Senior Scientist at Elder Research

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