Time Series
| Title | ΧΡΟΝΙΚΕΣ ΣΕΙΡΕΣ / Time Series |
| Code | 0564 |
| Faculty | Sciences |
| School | Mathematics |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Spring |
| Coordinator | Ourania Theodosiadou |
| Common | No |
| Status | Active |
| Course ID | 40000530 |
Programme of Study: Merikīs Foítīsīs (2014-sīmera)
Registered students: 3
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses | Spring | - | 5 |
Programme of Study: UPS of School of Mathematics (2014-today)
Registered students: 150
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Core | Elective Courses | 6 | 3 | 5 |
| Academic Year | 2025 – 2026 |
| Class Period | Spring |
| Instructors from Other Categories |
|
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600276436
|
Course Type 2011-2015
General Foundation
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600276436
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=12845
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
Learning Outcomes
Upon successful completion of the course, students will:
- be able to recognize the qualitative characteristics of time series, e.g. trend, periodicity, etc.
- have a comprehensive theoretical background on the basic methods of time series analysis
- have knowledge and critical understanding of the key properties of AR, MA, ARMA and ARIMA models
- can fit time series data to linear stochastic models using R programming language
- have a comprehensive theoretical background on the basic methods of time series prediction
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Appreciate diversity and multiculturality
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
Time series characteristics, stationarity, autocorrelation function, linear stationary stochastic models: AR (p), MA (q), ARMA (p, q), non-stationary ARIMA models (p, d, q), methodology of Box & Jenkins, prediction methods for time series.
Educational Material Types
- Notes
- Slide presentations
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 80 | ||||
| Fieldwork | |||||
| Reading Assigment | 40 | ||||
| Exams | 30 | ||||
| Total | 150 |
Student Assessment
Student Assessment methods
- Written Exam with Short Answer Questions (Formative, Summative)
- Written Exam with Extended Answer Questions (Formative, Summative)
- Written Exam with Problem Solving (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
- Σύγχρονες Μέθοδοι Ανάλυσης Χρονολογικών Σειρών, Σ. Δημέλη, Εκδόσεις ΟΠΑ, 2013
- Σημειώσεις "Ανάλυση Χρονοσειρών", Δ. Κουγιουμτζής, 2012 (http://users.auth.gr/dkugiu/Teach/TimeSeries/index.html)
Additional bibliography for study
"Introduction to time series and forecasting", Brockwell, P. J., Davis, R. A., Springer New York, 2002
"The Analysis of Time Series, An Introduction", Chatfield C., Sixth edition, Chapman & Hall, 2004
Last Update
16-02-2025
