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Course unit, curriculum year 2022–2023

Introduction to Time Series Analysis, 5 cr

Tampere University
Teaching periods
Active in period 1 (1.8.2022–23.10.2022)
Active in period 2 (24.10.2022–31.12.2022)
Active in period 3 (1.1.2023–5.3.2023)
Active in period 4 (6.3.2023–31.5.2023)
Course code
Language of instruction
English, Finnish
Academic years
2021–2022, 2022–2023, 2023–2024
Level of study
Intermediate studies
Grading scale
General scale, 0-5
Persons responsible
Responsible teacher:
Jari Turunen
Responsible organisation
Faculty of Information Technology and Communication Sciences 100 %
Coordinating organisation
Computing Sciences Studies 100 %
Common learning outcomes
Core content
  • Reduction of measurement noise in time series (eg average / weighted average), different filtering options
  • Understanding the concept of internal dependencies (correlation) of the time series, understanding the dependencies between several types of measurements
  • Change rate (differential) and automatic phenomena extraction from data and isolation of significant data from data
  • Prediction of data by linear correlation, estimation of missing data
Complementary content
  • Visualization of the phenomena both in time and frequency domains
  • Implementation of correlation search between the measurement sets.
  • Understand how phenomena can be searched automatically
Special knowledge
  • Understanding the Dependence Between Time and Frequency Domains
  • Knowing another prediction models and have ability to use them
Learning outcomes
Further information
Learning material
Studies that include this course
Completion option 1
Approved assignments

Participation in teaching

30.08.2022 31.12.2022
Active in period 1 (1.8.2022–23.10.2022)
Active in period 2 (24.10.2022–31.12.2022)
01.02.2023 29.04.2023
Active in period 3 (1.1.2023–5.3.2023)
Active in period 4 (6.3.2023–31.5.2023)