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Apply for Open University studies by using the following links:

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	<li><a href="https://tau-avoin.ilmoittaudu.fi/lobby/?realization=J-otm-bacd7a56-6d83-463f-b332-11449457165a_2023-pori-DATASTAT650" target="_blank">DATA.STAT.650 Introduction to Time Series Analysis, online teaching, Pori</a></li>
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You can find a link to the Open University Admissions service and application instructions on the <a href="https://www.tuni.fi/en/study-with-us/open-higher-education-studies/open-university/applying-open-university">Applying to Open University studies</a> page.

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When you have been admitted to the studies, you must enrol separately via the Sisu information system for the implementation of the unit of study and, for example if set, for a practise group of your choice. You will receive instructions on how to do this by e-mail after you have confirmed your acceptance of the study place by paying the required fee.

Pori

Introduction to Time Series Analysis, Online teaching

Contact information Email: open.studies.tau [at] tuni.fi Phone: 0294 520 200More information concerning Open University studies <a href="https://www.tuni.fi/open-university">tuni.fi/open-university</a>

Intermediate studies

Faculty of Information Technology and Communication Sciences

Data Processing and Information Technology

English

Online studies

Look information at <a href="https://www.tuni.fi/en/study-with-us/open-higher-education-studies/open-university/applying-open-university">Applying to Open University studies</a>.

<h5>Core content </h5><ul><li>Reduction of measurement noise in time series (eg average / weighted average), different filtering options </li><li>Understanding   the concept of internal dependencies (correlation) of the time series,   understanding the dependencies between several types of measurements</li><li>Change rate (differential) and automatic phenomena extraction from data and isolation of significant data from data</li><li>Prediction of data by linear correlation, estimation of missing data</li></ul><h5>Complementary content </h5><ul><li>Visualization of the phenomena both in time and frequency domains</li><li>Implementation of correlation search between the measurement sets.</li><li>Understand how phenomena can be searched automatically</li></ul><h5>Special knowledge </h5><ul><li>Understanding the Dependence Between Time and Frequency Domains</li><li>Knowing another prediction models and have ability to use them</li></ul>

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.

Finnish

Moodle<a href="https://moodle.tuni.fi/course/view.php?id=33906" target="_blank" rel="noopener noreferrer">https://moodle.tuni.fi/course/view.php?id&amp;#61;33906</a>

This course is remote study course and all the course material is in TUNI Moodle platform. Exercises can be made using Matlab, Python or other similar environments. There are no exams, but the exercises are evaluated and the final grading is based on the summed exercise points. Contact to the teacher is encouraged via email, audio or video communications or by personal contact.

Introduction to Time Series Analysis, Online teaching

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Introduction to Time Series Analysis, 5 cr

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Introduction to Time Series Analysis, Online teaching