Statistical analysis of time series
| Title | Στατιστική Ανάλυση Χρονοσειρών / Statistical analysis of time series |
| Code | ΦΠΕ206 |
| Faculty | Sciences |
| School | Physics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Kleareti Tourpali |
| Common | No |
| Status | Active |
| Course ID | 600022919 |
Programme of Study: PMS FYSIKĪ ATMOSFAIRIKOU PERIVALLONTOS KAI PAGKOSMIŌN METAVOLŌN
Registered students: 5
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Special Election | 2 | 1 | 4 |
| Academic Year | 2025 – 2026 |
| Class Period | Spring |
| Faculty Instructors |
|
| Weekly Hours | 2 |
| Total Hours | 26 |
| Class ID | 600286459
|
Course Type 2021
Specialization / Direction
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600286459
Language of Instruction
- Greek (Instruction, Examination)
Learning Outcomes
After succesfully completing the course the students will have to oporrtunity to
review the techniques used to identify and analyse patterns in time series data, seasonality and trends, using smoothing and curve fitting techniques and autocorrelations
get introduced to a general class of models commonly used to represent time series data and generate predictions (autoregressive and moving average models)
get acquainted to commonly used modeling and forecasting techniques based on linear regression. Analysis of spatial patterns.
General Competences
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work autonomously
- Work in teams
Course Content (Syllabus)
Time series analysis-Introduction-definitions. Identifying patterns in time series data. Stationarity and random noise.Trends, seasonality and their analysis. Linear and non-linear regression. ARIMA (Box and Jenkins), autocorrelation, autoregression. Spectral analysis (single-spectrum-Fourier, cross-spectrum, coherence). Spatial pattern analysis (Principal Component Analysis and EOF)
Keywords
time series;statistics;regression;autoregression;ARIMA;PCA;spectral analysis
Educational Material Types
- Notes
- Slide presentations
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Communication with Students
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 78 | 3.1 | |||
| Project | 22 | 0.9 | |||
| Total | 100 | 4 |
Student Assessment
Description
reports on projects;oral presentation
Student Assessment methods
- Written Assignment (Formative, Summative)
- Performance / Staging (Formative, Summative)
Bibliography
Additional bibliography for study
Σημειώσεις
1. H. von Storch and F. W. Zwiers, Statistical Analysis in Climate Research, Cambridge University Press, 1999.
2. Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1994). Time Series Analysis, Forecasting and Control, 3rd ed. Prentice Hall, Englewood Clifs, NJ.
Last Update
09-12-2023
