Demand Forecasting Principles with Examples in R

About the Course

Our course teaches you how to forecast demand in R. It is a 4-week online course, with sessions held on Tuesdays and Wednesdays, 2 hours a day, at timings to be confirmed, likely between 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: You will learn how to develop models and forecast using R, with demonstrations of appropriate methods of data analysis and forecasting.
• Business analysts: You will learn which models to use in different circumstances and how to select the most appropriate.
• PhD students and academics: If you want to improve your knowledge in forecasting principles, this course is for you.

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 doing this course, you 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 the point and interval forecasts

Meet Your Tutors

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

Testimonials

Hear from previous attendees:

• Ardalan Irani, PhD student at Kühne Logistics University, Germany
• Sanne de Roever, Data Scientist at Newfoundland, Netherlands
• Kasim Zor, Assistant Professor, Adana Alparslan Turkes Science & Technology University
• Athanasios Kontinopoulos: Economic Analysis and Research Department, Bank of Greece
• Steven van Aken, Consultant
• Leonidas Tsaprounis, Senior Data Scientist, Haleon

Course Prerequisites

R knowledge is desirable, but introductory materials will be provided if you 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, to attend, you need to register and pay for the course.
• 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, in November 2024 and May 2025.

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