Prediction and Time Series Analysis
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Course information
Learning outcomes
After passing the course the students can analyse and forecast time series using regression models and ARIMA-models. Students are able to apply linear regression model to analyse and forecast dependent variable under the model assumptions. In addition, the students are able to conduct diagnostic tests to validate the model assumptions. Students are familiar with the concept weakly stationary processes and they understand the most important related concepts including the autocorrelation function, and partial autocorrelation function. Students are also able to apply these functions in analysing real time series, for example, in recognising seasonal fluctuations. After taking the course, the students know ARIMA-models and their key properties. In addition, the students are able to model and predict the future behaviour of observed time series using ARIMA-models. The students also know basic concepts of dynamic regression models.
Content
The course is an introduction to time series analysis. Course topics include linear regression model and its diagnostics, central concepts of weakly stationary processes, ARIMA-models and their properties, stationarity of ARIMA-models, forecasting with ARIMA-models, Kalman filter, and introduction to dynamic regression models. Software R is used in the exercises of the course.
Teacher
Pauliina Ilmonen, Pekka Pere
Study material
Lecture slides and the textbook Peter J. Brockwell, Richard A. Davis: Time Series Theory and Methods, Springer 2009 (reprint of the 2nd edition 1991).
Workload
Lectures 24h (2), Exercises 24h (2), Homework assignments 48h, reading and studying the lecture materials 36h
Prerequisites
Recommended prerequisites: MS-A05XX First course in probability and statistics, MS-A02XX Differential and integral calculus 2, and MS-A00XX Matrix algebra.
Evaluation
Homework assignments, exercise points, exam
Teaching time and location
The teaching time and the location of the course can be found at Sisu information system
- The link leads to Completion methods tab of the course page
- From the top of the page (Version drop-down menu), check that academic year 2025-2026 is selected
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From the blue bar, click Lecture and open the Groups and teaching times tab to see the teaching time and the location of the course. Exam times can be seen by clicking Exam.
The teacher has four (4) weeks to grade the exam submissions starting from the exam date.
Further information
Equivalences to other courses: Mat-2.3128 Prediction and Time Series Analysis
Online learning environment for the course
MyCourses online learning environment is a tool for both students and teachers for communication and managing everyday course work. You need an Aalto User ID to log in to the course's MyCourses workspace and participate in teaching.
Digital workspace of the course:
MyCourses
More details on completing the course will be available at MyCourses closer to the start of the course.
Registration and course fee
Registration for Open University courses is done via the link below:
Course registration
Registration for this course starts on 22.9.2025 at 9.00
Registration for this course ends on 27.10.2025 at 23.59
It is not possible to register before the registration period has started or after it has ended.
The number of participants is limited to 10.
This course is organized by the Aalto University School of Science.
The course fee is paid upon registration for each course at a time and it is binding. Familiarize yourself with registration and payment rules as well as other guidelines for students:
Registration, payments and rules
Activating Aalto ID and Multifactor Authentication
You can activate your Aalto User ID the day after the registration and you should activate it no later than the day before the start of the course.
Instructions for activating your Aalto user ID
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Studying at the Open University
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