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.
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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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Forecasting Demand With Time Series
FreeWhen 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.
Loading data into xts object50 xpImporting data100 xpPlotting / visualizing data100 xpARIMA Time Series 10150 xpauto.arima() function100 xpInterpret auto.arima50 xpForecasting and Evaluating50 xpForecast function100 xpCalculating MAPE and MAE100 xpVisualizing Forecast100 xpConfidence Intervals for Forecast100 xp - 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.
Price elasticity50 xpCalculating price elasticity100 xpInterpret results from elasticity50 xpSeasonal / holiday / promotional effects50 xpVisualize holiday / promotion effects100 xpCreate holiday / promotional effect variables100 xpRegression for holiday / promotional effects100 xpSignificant holiday or promotional effects?50 xpForecasting with regression50 xpCreate future predictor variables100 xpForecast future values of demand100 xpVisualizing forecasts of regression100 xp - 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.
Residuals from regression model50 xpCalculating residuals from regression100 xpVisualizing residuals100 xpForecasting residuals50 xpauto.arima function100 xpForecasting residuals with time series100 xpVisualizing residual forecasts100 xpTransfer functions & ensembling50 xpCombining residuals from regression & time series100 xpVisualizing transfer function forecast100 xpCalculating transfer function MAPE and MAE100 xpARIMA Forecasting100 xpEnsembling of Forecasts100 xp - 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.
Bottom-Up Hierarchical Forecasting50 xpBuild time series forecast for new product100 xpBuild regression forecast for new product100 xpEnsemble forecast for new product100 xpCalculate bottom-up forecast for whole region100 xpTop-Down Hierarchical Forecasting50 xpBuild time series forecast at regional level100 xpUsing average historical proportions100 xpUsing proportion of historical averages100 xpMiddle-Out Hierarchical Forecasting50 xpBuild time series forecast for new region100 xpTop-down forecast for new region100 xpBottom-up forecast for whole state100 xpCongratulations!50 xp
Datasets
Beverage producer salesCollaborators
Prerequisites
Case Study: Analyzing City Time Series Data in RAric LaBarr
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Director and Senior Scientist at Elder Research
A Teaching Associate Professor in the Institute for Advanced Analytics, Dr. Aric LaBarr is passionate about helping people solve challenges using their data. There he helps design the innovative program to prepare a modern work force to wisely communicate and handle a data-driven future at the nation's first master of science in analytics degree program. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management.
Previously, he was Director and Senior Scientist at Elder Research, where he mentored and lead a team of data scientists and software engineers. As director of the Raleigh, NC office he worked closely with clients and partners to solve problems in the fields of banking, consumer product goods, healthcare, and government.
Dr. LaBarr holds a B.S. in economics, as well as a B.S., M.S., and Ph.D. in statistics — all from NC State.
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