PLA-43136 Time Series Analysis, 5 cr
Additional information
The course can be completed with two different implementation methods. The course provides traditional lectures and conducts assignments. However, the teaching material is available on Moodle platform, so it is also possible to complete the study period independently of time and place throughout the academic year. If the student intends to complete the course outside of the lecture period, he / she should contact the person in charge, jari.j.turunen (at) tut.fi, for obtaining course IDs.
Person responsible
Jari Turunen
Learning Outcomes
After completing the course, students are able to search for phenomena from different time series (sensor data, historical time series, economic and productive time series), clean data and search dependencies between time series related to the same phenomenon, such as temperature and atmospheric pressure. The course is completed by assignments.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Reduction of measurement noise in time series (eg average / weighted average), different filtering options | Visualization of the phenomena both in time and frequency domains | Understanding the Dependence Between Time and Frequency Domains |
| 2. | Understanding the concept of internal dependencies (correlation) of the time series, understanding the dependencies between several types of measurements | Implementation of correlation search between the measurement sets. | |
| 3. | Change rate (differential) and automatic phenomena extraction from data and isolation of significant data from data | Understand how phenomena can be searched automatically | |
| 4. | Prediction of data by linear correlation, estimation of missing data | Knowing the AR model well, and knowing where and when to use it | Knowing another prediction models such as AR,MA and AR(I)MA linear models well and have ability to use them |
Instructions for students on how to achieve the learning outcomes
Approved assignments
Assessment scale:
Numerical evaluation scale (0-5)
Partial passing:
Correspondence of content
| Course | Corresponds course | Description |
| PLA-43136 Time Series Analysis, 5 cr | PLA-43131 Time Series Analysis, 5 cr |