PřF:Bi6446 Time Series Forecasting - Course Information

Bi6446 Time Series Forecasting

Extent and Intensity

2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).

Teacher(s)

prof. Ing. Jiří Holčík, CSc. (lecturer)

Guaranteed by

prof. Ing. Jiří Holčík, CSc.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Epidemiology and modeling (programme PřF, N-MBB)
• Mathematical Biology (programme PřF, N-EXB)
• Modelling and Calculations (programme PřF, B-MA)

Course objectives

At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series prediction not only with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics

Learning outcomes

At the end of the course, students should be able to:
- know fundamental theoretical and methodological principles of methods of time series spectral analysis with emphasis to biological data processing
- explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms;
- apply different practical approaches to data processing to obtain required analytic results;
- design modified algorithms to process data of given particular characteristics.

Syllabus

• 1. Why prediction usually fails.
• 2. Prediction – what is it?, preliminary analysis, transformation & adjustments, prediction models - method of simple forecasting.
• 3. Prediction models – regression, linear prediction (autoregressive models, moving average models).
• 4. Prediction models – linear prediction (exponential smoothing).
• 5. Judgemental forecasting.
• 6. Forecasting evaluation

Teaching methods

Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.

Assessment methods

oral examination

Language of instruction

Czech

Further comments (probably available only in Czech)

The course is taught annually.
General note: Vhodné je mít základy metod zpracování signálů a spektrální analýzy.

• Enrolment Statistics (Spring 2021, recent)
  
• Permalink: https://is.muni.cz/course/sci/spring2021/Bi6446