Topic outline

  • Please note that all the lectures and exercises of this course are given in zoom. Please note that neither the lectures nor the exercises are recorded, but the lecture slides are posted under "Materials"! Please note also that homework assignments are part of the grading. Please submit your solutions on time!


    This 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.

    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, partial autocorrelation function, and the spectral function. Students are also able to apply these functions in analysign real time series, for example, in recognizing 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.

    Language of Instruction: English, All the lecture materials, exercises, exams etc are in English only!

    Workload: Exercises 24h (2), Lectures 24h, Homework assignments 48 h, reading and studying the lecture materials 36 h

    Assessment Methods and Criteria: Homework assignments, exam

    Study Material: Lecture slides 

    Substitutes for Courses: Mat-2.3128 Prediction and Time Series Analysis

    Prerequisites: MS-A05XX First course in probability and statistics, MS-A02XX Differential and integral calculus 2, and MS-A000X Matrix algebra

    Evaluation: 1-5


    Lectures are given in zoom. Lectures are an important part of this course. Attendance to zoom lectures is not compulsory, but highly recommended. Note that the lectures are not recorded! Lecture slides are posted on MyCourses webpage. Please study the slides carefully before solving the corresponding exercises.


    Exercises are given in zoom. Exercises are an important part of this course. Attendance to zoom exercises is not compulsory, but highly recommended. Note that the zoom exercises are not recorded! You get exercise points by doing your homework assignments and submitting them on time. Zoom exercises might help in solving the problems and correct solutions to homework problems are provided in the online exercises only. The exercise points are valid until the end of June 2021.

    There are several exercise groups. Please attend one of the theory exercise groups and one of the computer exercise groups.

    How to pass this course?

    You are expected to

    Study the lecture slides carefully.

    Participate to exercises and solve your homework problems - not compulsory, but highly recommended - max 12 points.

    Take the exam - max 24 points.

    Max total points = 24 + 12 = 36: You need at least 18 points (or 12 points from the exam) in order to pass the course.

    How to get a good grade?

    Work hard on your homework assignments.

    Be active in the exercises!

    Study for the exam!