# Time Series Analysis

 Title ΧΡΟΝΟΣΕΙΡΕΣ / Time Series Analysis Code ΜΑ0601 Faculty Engineering School Electrical and Computer Engineering Cycle / Level 1st / Undergraduate Teaching Period Winter Coordinator Dimitris Kugiumtzis Common No Status Active Course ID 20000620

 Academic Year 2014 – 2015 Class Period Winter Faculty Instructors Weekly Hours 4 Class ID 20052194
Course Type 2016-2020
• Background
• General Knowledge
Course Type 2011-2015
General Foundation
Mode of Delivery
• Face to face
Digital Course Content
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
• Greek (Instruction, Examination)
• English (Instruction, Examination)
Prerequisites
Required Courses
• ΜΑ0301 Probability and Statistics
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 Laboratory Teaching
Description
Worked examples/exercises, lab on Matlab
Course Organization
Lectures
Laboratory Work
Total
Student Assessment
Description
1. Written examination of 180 minutes. 2. Assignments of exercises on Matlab during the course
Student Assessment methods
• Performance / Staging (Summative)
• Written Exam with Problem Solving (Summative)
• Labortatory Assignment (Summative)
Bibliography
Course Bibliography (Eudoxus)
1. Σημειώσεις "Ανάλυση Χρονοσειρών", Δ. Κουγιουμτζής, 2012. 2. Time Series Analysis and Its Applications [electronic resource], Shumway, R. H. and Stoffer, D. S, Springer 3. Introduction to Modern Time Series Analysis [electronic resource], Kirchgässner G., Wolters J., Hassler U., Springer 4. The Analysis of Time Series, An Introduction, Chatfield C., Sixth edition, Chapman & Hall, 2004 5. Introduction to time series and forecasting, Brockwell P.J. and Davis R.A., Second edition, Springer, 2002 6. Non-Linear Time Series, A Dynamical System Approach, Tong H., Oxford University Press, 1993 7. Nonlinear Time Series Analysis, Kantz H. and Schreiber T., Cambridge University Press, 2004 8. Applied Nonlinear Time Series Analysis: Applications in Physics, Physiology and Finance, Michael Small, World Scientific