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Course

Forecasting Product Demand in R

IntermediateSkill Level

4.6+

Updated 11/2022

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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RProbability & Statistics4 hr13 videos50 Exercises4,200 XP9,423Statement of Accomplishment

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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.

Prerequisites

Case Study: Analyzing City Time Series Data in R

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.

Loading data into xts object

Importing data

Plotting / visualizing data

ARIMA Time Series 101

auto.arima() function

Interpret auto.arima

Forecasting and Evaluating

Forecast function

Calculating MAPE and MAE

Visualizing Forecast

Confidence Intervals for Forecast

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 elasticity

Calculating price elasticity

Interpret results from elasticity

Seasonal / holiday / promotional effects

Visualize holiday / promotion effects

Create holiday / promotional effect variables

Regression for holiday / promotional effects

Significant holiday or promotional effects?

Forecasting with regression

Create future predictor variables

Forecast future values of demand

Visualizing forecasts of regression

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 model

Calculating residuals from regression

Visualizing residuals

Forecasting residuals

auto.arima function

Forecasting residuals with time series

Visualizing residual forecasts

Transfer functions & ensembling

Combining residuals from regression & time series

Visualizing transfer function forecast

Calculating transfer function MAPE and MAE

ARIMA Forecasting

Ensembling of Forecasts

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 Forecasting

Build time series forecast for new product

Build regression forecast for new product

Ensemble forecast for new product

Calculate bottom-up forecast for whole region

Top-Down Hierarchical Forecasting

Build time series forecast at regional level

Using average historical proportions

Using proportion of historical averages

Middle-Out Hierarchical Forecasting

Build time series forecast for new region

Top-down forecast for new region

Bottom-up forecast for whole state

Congratulations!

Forecasting Product Demand in R

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FAQs

Is this course suitable for beginners?

Yes! This course is designed for learners of all levels of experience, from beginner to experienced. All the necessary concepts and tools will be explained in detail, so you don't need any prior knowledge.

Will I receive a certificate at the end of the course?

Yes! After completing the course, you will be eligible to receive an official certificate from DataCamp.

Who will benefit from this course?

This course would be beneficial to anyone looking to drive product demand such as operations managers, supply chain analysts, sales and marketing personnel, and pricing and merchandising analysts.

How will this course help me forecast product demand?

This course is designed to help you accurately forecast product demand in an efficient and reliable way. You will learn to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real-world example.

What topics are covered in this course?

This course covers a broad range of topics related to forecasting product demand, including time series modeling, pricing elasticity models, transfer functions and forecast ensembling, and hierarchical forecasting.

What software will I be using in this course?

This course primarily uses the R language to build models and use functions.

What technologies and datasets will I encounter in this course?

You will encounter datasets about sales and customer data as well as various analytical and plotting technologies.

What results can I expect from completing this course?

After carefully following the course, you will be able to predict demand for multiple products across a region of a state, roll up these predictions across many different regions of the same state, and form a complete hierarchical forecasting system.

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