Time Series

Course Information
TitleΧρονοσειρές / Time Series
SchoolElectrical and Computer Engineering
Cycle / Level1st / Undergraduate
Teaching PeriodWinter
CoordinatorDimitris Kugiumtzis
Course ID600001002

Programme of Study: Electrical and Computer Engineering

Registered students: 19
OrientationAttendance TypeSemesterYearECTS
ELECTRICAL ENERGYElective Courses744

Class Information
Academic Year2019 – 2020
Class PeriodWinter
Faculty Instructors
Class ID
Course Type 2016-2020
  • Background
  • General Knowledge
Course Type 2011-2015
General Foundation
Mode of Delivery
  • Face to face
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
  • English (Instruction, Examination)
Learning Outcomes
1. Understanding the significance of time series analysis in engineering science. 2. Acquaintance with the investigation of stochastic processes and dynamical systems from time series as well as prediction of time series. 3. Understanding the complications in the analysis of real-world time series and how they are addressed, such as non-stationarity and non-normal stochastic process. 4. Acquaintance with computational approaches for the solution of problems in time series analysis. 5. Ability of analyzing real time series in the computer.
General Competences
  • Apply knowledge in practice
  • Work autonomously
  • Work in teams
Course Content (Syllabus)
Basic characteristics of time series: stationarity; autocorrelation; removal of trends and seasonality; independence test of time series. Linear stochastic processes: autoregressive (AR), moving average (MA), autoregressive moving average (ARMA). Time series models: AR, MA and ARMA for stationary time series; autoregressive integrated moving average (ARIMA) models and seasonal ARIMA (SARIMA) for non-stationary time series. Prediction of time series. Nonlinear analysis of time series: Extensions of linear stochastic models; nonlinear characteristics of time series; nonlinear dynamics and chaos; nonlinear prediction of time series.
Educational Material Types
  • Notes
  • Slide presentations
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
Worked examples/exercises, lab on Matlab. Exercise deposition and communication through elearning.
Course Organization
Laboratory Work130.4
Student Assessment
1. Written examination of 180 minutes. 2. Optional assignments of exercises on Matlab during the course 3. Presentation of a pre-assigned topic.
Student Assessment methods
  • Performance / Staging (Summative)
  • Written Exam with Problem Solving (Summative)
  • Labortatory Assignment (Summative)
Course Bibliography (Eudoxus)
1. Χαοτικές Χρονοσειρές: Θεωρία και Πράξη Κωδικός Βιβλίου στον Εύδοξο: 50659162) Έκδοση: 1/2001 Συγγραφείς: Παπαϊωάννου Γεώργιος, ISBN: 9607901053, Διαθέτης (Εκδότης): LIBERAL BOOKS ΜΟΝΟΠΡΟΣΩΠΗ ΕΠΕ 2. Introduction to Time Series and Forecasting [electronic resource] Κωδικός Βιβλίου στον Εύδοξο: 75487888, Third Edition/2016, Συγγραφείς: Peter J. Brockwell / Richard A. Davis ISBN: 9780387216577, Τύπος: Ηλεκτρονικό Βιβλίο, Διαθέτης (Εκδότης): HEAL-Link Springer ebooks 3. Introduction to Modern Time Series Analysis [electronic resource] Κωδικός Βιβλίου στον Εύδοξο: 73243237, Έκδοση: 2nd ed. 2013/2013 Συγγραφείς: Gebhard Kirchgassner / Jurgen Wolters / Uwe Hassler ISBN: 9783642334368, Διαθέτης (Εκδότης): HEAL-Link Springer ebooks
Additional bibliography for study
1. The Analysis of Time Series, An Introduction, Chatfield C., Sixth edition, Chapman & Hall, 2004 2. Nonlinear Time Series Analysis, Kantz H. and Schreiber T., Cambridge University Press, 2004 4. Applied Nonlinear Time Series Analysis: Applications in Physics, Physiology and Finance, Michael Small, World Scientific
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