Demand Forecasting Principles with examples in R

Online course for data scientists and demand planners

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About the course

Our course teaches you how to forecast demand in R.

4-week online course

The course is held online over 4 weeks. Its sessions are on Tuesdays and Wednesdays, 2 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.

Course dates

  • Week one: Tuesday 4th and Wednesday 5th November
  • Week two: Tuesday 11th and Wednesday 12th November
  • Week three: Tuesday 18th and Wednesday 19th November
  • Week four: Tuesday 25th and Wednesday 26th November.
Register
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Key features

We cover basics of business forecasting. We will use business context and discuss the conventional forecasting approaches that are usually used in it.

We teach forecasting fundamentals. You will learn how to model real life problems, not just how to create R code.

We show how to solve problems in R. You will understand which functions are appropriate for different problems and how the functions can help you solve problems.

We know applied 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?

PhD students and academics. If you want to improve your knowledge in forecasting principles (even if you have none), this course is for you. 

Demand planners. Experts and novices will learn the principles of demand forecasting and how to put them into practice.

Data scientists. You will learn how to develop models and forecast using R. Appropriate methods of data analysis and forecasting will be demonstrated.

Business analysts. You will learn which models to use in different circumstances and how to select the most appropriate.

Course content

The topics for the course will be selected from the following list.

  • 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:

  • Know forecasting principles;
  • Identify time series components;
  • Analyse time series structure;
  • Understand how forecasting models work;
  • Understand what parameters of models mean;
  • 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

Testimonials

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.

Kasim Zor, Assistant Professor, Adana Alparslan Turkes Science & Technology University

I enjoyed quizzes and workshops. Every question that we asked was answered comprehensively. Appreciated! [Kasim]

Anonymous

I really liked the explanations about ETS in details and the combinations approaches. All of them are delivered well and very informative.

Steven van Aken, Consultant

The course used real-life examples with strong underpinning theories. The course provided well-rounded teaching materials, and the tutors are knowledgeable. I appreciated the tutors taking time for Questions and Answers.

Leonidas Tsaprounis, Senior Data Scientist, Haleon

The tutors are true experts in statistical forecasting models. Their insights on the simplest aspects of the field were valuable, even for a seasoned practitioner like myself. This course gave me a better understanding of forecasting methods and best practices. This course motivates me to learn forecasting more!

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.

Register

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

£500 per person

Group plan

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

£400 per person

Choose your plan and secure your place.

Register for the online course

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

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Email

Contact us by email: cmaf@lancaster.ac.uk

Other ways to get in touch

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