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 | 2018 – 2019 |
| Class Period | Winter |
| Faculty Instructors |
|
| Weekly Hours | 4 |
| Class ID | 600130535
|
Class Schedule
| Building | Πολυτεχνείο - πτέρυγα Γ (ΤΗΜΜΥ & Τοπογράφων Μηχ.) |
| Floor | Όροφος 1 |
| Hall | Α4 (5) |
| Calendar | Παρασκευή 13:00 έως 16:00 |
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
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | |||||
| Laboratory Work | |||||
| Reading Assigment | |||||
| 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
Additional bibliography for study
Teacher’s notes in Greek, also
1. Time Series Analysis and Its Applications [electronic resource], Shumway, R. H. and Stoffer, D. S, Springer
2. Introduction to Modern Time Series Analysis [electronic resource], Kirchgässner G., Wolters J., Hassler U., Springer
3. The Analysis of Time Series, An Introduction, Chatfield C., Sixth edition, Chapman & Hall, 2004
4. Introduction to time series and forecasting, Brockwell P.J. and Davis R.A., Second edition, Springer, 2002
5. Non-Linear Time Series, A Dynamical System Approach, Tong H., Oxford University Press, 1993
6. Nonlinear Time Series Analysis, Kantz H. and Schreiber T., Cambridge University Press, 2004
7. Applied Nonlinear Time Series Analysis: Applications in Physics, Physiology and Finance, Michael Small, World Scientific
Last Update
13-03-2015
