Demand Forecasting Principles with Examples in R

About the Course

Our course teaches students how to forecast demand in R. The course is held online over 4 weeks, with sessions on Tuesdays and Wednesdays, 2 hours a day. Timings are to be confirmed, but are likely to be 2-4 pm UK time.

Course Dates

• Week one: Tuesday 3rd and Wednesday 4th November 2026
• Week two: Tuesday 10th and Wednesday 11th November 2026
• Week three: Tuesday 17th and Wednesday 18th November 2026
• Week four: Tuesday 24th and Wednesday 25th November 2026

Key Features

• We cover the basics of business forecasting, using business context and discussing conventional forecasting approaches.
• We teach forecasting fundamentals, focusing on modeling real-life problems, not just creating R code.
• We show how to solve problems in R, explaining which functions are appropriate for different problems and how they can help solve them.
• We know applied forecasting, going beyond R to address practical problems.

Who is the Course For?

• Demand planners: Experts and novices will learn the principles of demand forecasting and how to put them into practice.
• Data scientists: Students will learn how to develop models and forecast using R, with demonstrations of appropriate methods of data analysis and forecasting.
• Business analysts: Students will learn which models to use in different circumstances and how to select the most appropriate.
• PhD students and academics: This course is suitable for those who want to improve their knowledge in forecasting principles, even if they have no prior knowledge.

Course Content

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

• Forecasting principles
• Time series components
• Linear regression
• Advanced modeling approaches
• Advanced forecasting methods

Forecasting Principles

• 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

Time Series Components

• 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

Linear Regression

• Simple linear regression
• Multiple linear regression
• Regression diagnostics
• Transformation of variables
• Variables selection
• Using regression in promotional modeling

Advanced Modeling Approaches

• ETS with explanatory variables
• Multiple frequencies
• Model and forecast selection
• Combination of forecasts
• Judgment and organizational aspects of forecasting

Advanced Forecasting Methods

• Intermittent demand forecasting
• ARIMA
• Hierarchical forecasting: cross-sectional and temporal hierarchies

Learning Outcomes

By completing this course, students will be able to:

• Know forecasting principles
• Identify time series components
• Analyze 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 point and interval forecasts

Meet Your Tutors

• Dr. Ivan Svetunkov
• Dr. Kandrika Pritularga
• Dr. Sven Crone

Testimonials

Previous attendees have praised the course for its comprehensive coverage of forecasting topics, interactive quizzes and workshops, and knowledgeable tutors.

Course Prerequisites

R knowledge is desirable, but introductory materials will be provided for those who do not know R. No prior knowledge of forecasting or statistics is required.

Questions and Answers

• How long does the course last? Four weeks.
• Do I have to pay in advance? Yes, registration and payment are required to attend.
• What is the language of the course? The course will be delivered in English, with all supplementary materials also in English.
• Have you run this course before? Yes, the course has been run previously in November 2024 and May 2025.

Tuition Fees

• Basic plan: £500 per person, including access to course slides, workshop materials, communication with tutors, and a CMAF certificate upon successful completion.
• Group plan: £400 per person for 3 or more participants, including all benefits of the basic plan.

Lancaster University

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Overview:

Lancaster University is a public research university located in Lancaster, England. It is consistently ranked among the top 10 universities in the UK and is recognized for its high-quality teaching, research, and student experience.

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Student Life and Campus Experience:

Lancaster University offers a vibrant and diverse campus experience. Students can enjoy a range of facilities, including a library, sports center, and arts venues. The university also has a strong sense of community, with a variety of student societies and clubs to join.

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Key Reasons to Study There:

High Rankings and Reputation:

Lancaster University is consistently ranked among the top 10 universities in the UK, demonstrating its academic excellence.

Excellent Teaching and Learning:

The university is known for its high-quality teaching and learning, with a focus on student engagement and support.

Vibrant Student Life:

Lancaster University offers a wide range of opportunities for students to get involved in extracurricular activities, including sports, societies, and arts events.

Beautiful Campus:

The university is situated on a beautiful campus with modern facilities and green spaces.

Strong Career Support:

Lancaster University provides excellent career support services to help students prepare for their future careers.

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Academic Programs:

Lancaster University offers a wide range of undergraduate and postgraduate programs across various disciplines, including: * Arts and Social Sciences * Health and Medicine * Management School * Science and Technology