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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.

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4 Hours13 Videos50 Exercises7,421 Learners4200 XP

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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.

  1. 1

    Forecasting demand with time series


    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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    Loading data into xts object
    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.

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  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.

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  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.

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Beverage producer sales


yashasYashas Roy
Aric LaBarr Headshot

Aric LaBarr

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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