Time Series Analysis
| Title | ΑΝΑΛΥΣΗ ΧΡΟΝΟΣΕΙΡΩΝ / Time Series Analysis |
| Code | 0747 |
| Faculty | Sciences |
| School | Mathematics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Spring |
| Coordinator | Dimitris Kugiumtzis |
| Common | Yes |
| Status | Active |
| Course ID | 40002465 |
Programme of Study: PMS Tmīmatos Mathīmatikṓn (2018-sīmera)
Registered students: 14
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| STATISTIKĪ KAI MONTELOPOIĪSĪ | Compulsory Course | 2 | 1 | 10 |
| Academic Year | 2018 – 2019 |
| Class Period | Winter |
| Faculty Instructors | |
| Weekly Hours | 3 |
| Class ID | 600125738
|
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Specific Foundation / Core
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)
Learning Outcomes
The scope of the course is the introduction of concepts and methods of time series analysis, as well as their application to real problems with time series data. Within the framework of application the scope is the use of relevant software.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Work autonomously
- Work in teams
- Generate new research ideas
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. Spectral analysis 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.
Keywords
Time series, statistics, dynamical systems
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
Description
A practical lab on the computational environment Matlab.
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 40 | 1.3 | |||
| Laboratory Work | 8 | 0.3 | |||
| Topic presentations | 3 | 0.1 | |||
| Total | 51 | 1.7 |
Student Assessment
Description
Written exams: 50% of the final mark
Project on the basis of the computational environment Matlab: 30% of the final mark
Presentation of a special topic in time series analysis: 20% of the final mark
Student Assessment methods
- Written Assignment (Formative, Summative)
- Oral Exams (Formative, Summative)
- Performance / Staging (Formative, Summative)
- Written Exam with Problem Solving (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
1. Σημειώσεις "Ανάλυση Χρονοσειρών", Δ. Κουγιουμτζής, 2012 (δες http://users.auth.gr/dkugiu/Teach/TimeSeries/index.html)
Additional bibliography for study
1. “The Analysis of Time Series, An Introduction”, Chatfield C., Sixth edition, Chapman & Hall, 2004
2. “Introduction to time series and forecasting”, Brockwell P.J. and Davis R.A., Second edition, Springer, 2002
3. “Non-Linear Time Series, A Dynamical System Approach”, Tong H., Oxford University Press, 1993
4. “Nonlinear Time Series Analysis”, Kantz H. and Schreiber T., Cambridge University Press, 2004
Last Update
25-09-2018
