Applied Forecasting in Complex Systems

Course manual 2025/2026

Course content

From the beginning, students will learn how to analyze and forecast the dynamics of large-scale real-complex time-series data, from social or technological systems and forecast. Students will learn how to describe time-series prediction methods and the applications of these methods to different types of data in various contexts. Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts, and often it is necessary to provide distributional rather than point forecasts.  As such, an introduction to methods for probabilistic forecasting will also be provided. Time series modeling techniques will be considered with reference to their use in forecasting where suitable. While linear models will be examined in some detail, extensions to non-linear models using machine learning approaches will also be considered for real-world problems. In the course, students will learn the theoretical concepts during the lectures as exploring different problem domains, build up their skills by practicing on assignments, and finally demonstrate their knowledge and skills by a final project.

Study materials

Literature

• Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia.

• Joseph, Manu. (2022) Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning. Packt Publishing Ltd, 2022

Syllabus

• A syllabus containing articles and chapters will be made available at the beginning of the course. The syllabus/course will cover parts of the books proposed in the literature.

Software

• R and Python

Objectives

• Explain to what extent results derived from forecasts are useful to the problem at stake.
• Examine and interpret modeling and forecasting results critically.
• Assess the potential setup of forecasting models.
• Assess the visualization of uncertainty attaching to forecasts, and how it relates and differentiates from point forecasts.
• Design the most appropriate forecasting method and devise time series prediction models suitable for solving real-problem.

Teaching methods

• Lecture
• Computer lab session/practical training
• Self-study
• Seminar

Learning activities

Attendance

In TER part B of this programme no requirements regarding attendance are mentioned.

Additional requirements for this course:

Absence needs to be communicated to the course coordinator when not doing the course.

Assessment

• The course grade G is based on the quiz grades Q1, Q2, Q3, assignment grades A1, A2,, A3  and the Final Project grade FP. 
• If an assignment is not submitted, it will be graded as 0.

• 3 online quizzes (their average = 20% in total), Assignment 1 (individual) 15%,  Assignment 2 (individual) 15%, Assignment 3 (group) 15%, Final Project (group) (35%).  Hence, the individual component (50%) + group component  (50%) = 100%

• To pass, the grades have to be 5.50 or higher in both the individual and the group components (i.e., minimum 5.50 on both the average of "the Final Project and the group assignment", and  the average of "individual assignment and the quizzes").

Inspection of assessed work

Once it is announced, the student can inspect their work and see the feedback. Student can ask questions to TA's or the coordinator through email or in the class

Assignments

There are three quizzes (individual),  three assignments (two of them are individual, and one is a group assignment) and a Final Project (in group), in which a paper report is to be submitted. 

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Contact information

Coordinator

• dr. rer. nat. Erman Acar