Demand Forecasting with R

Online course for demand planners and data scientists

About the course

Our course teaches you how to forecast demand in R.

One-week online course

The course will be held online over a week, two hours a day.

Real life examples

Examples are based on real life data and will be relevant to your problems.

Delivered by professionals

The course is delivered by leading experts in the field of forecasting, members of the CMAF and their colleagues.

Interested? Email us to enquire about the course.

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Data analyst looking at a data visualisation

Key features

  • We teach forecasting fundamentals. You will learn how to model real life problems, not just how to create R code.
  • We show how to model problems in R. You will understand which functions are appropriate for different problems and how the functions can help you solve problems.
  • We know practicalities of forecasting. This forecasting course is not taught in a vacuum; we know what problems you face in practice and we will go beyond R.

Who is the course for?

Course content

The topics for the course will be selected from the list below, depending on your company needs and preferences. Overall, the course will last for a work week, 2 hours per day.

  • What to do and what not to do in forecasting,
  • Evaluating forecasting accuracy via error measures,
  • Uncertainty, prediction intervals and their evaluation,
  • How to inform decisions based on forecasting.
  • Classical time series decomposition,
  • Simple forecasting methods (Naïve, Global Average, Moving Average),
  • Exponential smoothing,
  • Introduction to the ETS model,
  • Holt, Holt-Winters and Damped trend methods and their connection with ETS.
  • Simple linear regression,
  • Multiple linear regression,
  • Regression diagnostics,
  • Transformation of variables,
  • Variables selection,
  • Using regression in promotional modelling.
  • ETS with explanatory variables,
  • Multiple frequencies,
  • Model and forecast selection,
  • Combination of forecasts,
  • Judgment and organisational aspects of forecasting.
  • Intermittent demand forecasting,
  • ARIMA,
  • Hierarchical forecasting: cross-sectional and temporal hierarchies.

Learning outcomes

By doing this course you will be able to:

  • Understand how forecasting models work;
  • Understand what parameters of models mean;
  • Analyse time series structure;
  • Identify time series components;
  • Produce point forecasts and prediction intervals for any time series;
  • Evaluate the accuracy of different forecasting methods;
  • Make relevant managerial decisions based on the point and interval forecasts.

Meet your tutors


Hear from previous attendees:

Athanasios Kontinopoulos: Economic Analysis and Research Department, Bank of Greece

I found the course really helpful, as it not only covered both basic and advanced concepts in forecasting, but it was also full of examples in R. The tutors were a strong asset, as they were very knowledgeable and provided great feedback.

Eric Ling: Head Of Forecasting, Supply Division of Howdens Joinery Ltd

Following on from an audit that the team from Lancaster University conducted for us on how we were using SAP APO Demand Planning, they developed a tailored workshop for our entire Forecasting Team on Statistical Forecast Modelling Techniques.

Attend on Zoom

We will deliver lecture materials, have discussions and see how forecasting can be done for a variety of cases. This will be done over Zoom with our tutors helping online at every step.

Choose from three options

Basic plan

  • Access to the slides of the course
  • Access to all workshop materials
  • Communication with course tutors during the course
  • CMAF certificate awarded upon successful completion


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Group plan

  • All the benefits of the basic plan
  • Discount for 3 or more participants

£500 per person

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Turbo plan

  • All benefits of the basic plan
  • Support for a short project immediately after the training
  • International Institute of Forecasters certificate awarded in case of successful completion of the course. Ask us for details about this.
  • Physical copy of textbook of Ord, Fildes & Kourentzes (2017)


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Course prerequisites

R knowledge is desirable, but we will provide introductory materials if you do not know R. The rest will be covered in the course.

  • You are not required to know anything about forecasting - we will teach you;
  • You are not expected to know statistics - we will explain the essentials;
  • You do not need to be a programming language expert - we will support you.

Questions and Answers


How to participate

In order for the course to run, we need to have at least 6 participants. Email us and we will inform you when this can happen.

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Get in touch

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