# Time Series

 Title Χρονοσειρές / Time Series Code 053 Faculty Engineering School Electrical and Computer Engineering Cycle / Level 1st / Undergraduate Teaching Period Winter Coordinator Dimitris Kugiumtzis Common No Status Active Course ID 600001002

### Programme of Study: Electrical and Computer Engineering

Registered students: 59
OrientationAttendance TypeSemesterYearECTS
ELECTRICAL ENERGYElective Courses744
ELECTRONICS AND COMPUTER ENGINEERINGElective Courses744
TELECOMMUNICATIONSElective Courses744

 Academic Year 2022 – 2023 Class Period Winter Faculty Instructors Dimitris Kugiumtzis 4hrs Weekly Hours 4 Class ID 600218220
Course Type 2021
Specialization / Direction
Course Type 2016-2020
• Background
• General Knowledge
Course Type 2011-2015
General Foundation
Mode of Delivery
• Face to face
Erasmus
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
Description
Worked examples/exercises, lab on Matlab. Exercise deposition and communication through elearning.
Course Organization
Lectures41.51.4
Laboratory Work130.4
Tutorial41.51.4
Exams240.8
Total1204
Student Assessment
Description
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)
Bibliography
Course Bibliography (Eudoxus)
1. Linear Time Series with MATLAB and OCTAVE [electronic resource] Víctor Gómez, HEAL-Link Springer ebooks, 2019, ISBN: 9783030207908 (Εύδοξος: 91691743) 2. Introduction to Time Series and Forecasting [electronic resource] Peter J. Brockwell / Richard A. Davis, HEAL-Link Springer ebooks, Third Edition/2016, (Εύδοξος: 75487888) 3. Introduction to Modern Time Series Analysis [electronic resource] Gebhard Kirchgassner / Jurgen Wolters / Uwe Hassler HEAL-Link Springer ebooks, 2nd ed. 2013, ISBN: 9783642334368 (Εύδοξος: 73243237)