Time series analysis
Data on various phenomena observed over time are common in virtually all sciences. Time series analysis involves drawing conclusions about the properties of time series and their development over time, typically to predict future values. Examples of applications are found in economics, finance, psychology, weather forecasting, control engineering, pattern recognition, etc.
The course provides basic knowledge of the theory and applications of statistical methods in time series analysis and skills in practical analysis of time series data. Throughout the course, great emphasis is placed on a critical approach to the use of statistical methods and the interpretation of results. Special emphasis is placed on the ability of different models to generate forecasts. Great importance is also placed on practical data handling, visualization, and analysis through programming in R.
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Course structure
The course is given at day time, full time.
Teaching format
The teaching consists of lectures, exercises, and computer exercises.
Course information
Course description fall 2024 (197 Kb)
Assessment
The course is examined through an individual written exam and a group assignment.
Examiner
Teachers fall 2024
Course coordinator
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Schedule
The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course. -
Course literature
Note that the course literature can be changed up to two months before the start of the course.
