Time Series
| Title | ΧΡΟΝΙΚΕΣ ΣΕΙΡΕΣ / Time Series |
| Code | 0564 |
| Faculty | Sciences |
| School | Mathematics |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Spring |
| Coordinator | Kostas Loumponias |
| Common | No |
| Status | Active |
| Course ID | 40000530 |
Programme of Study: UPS of School of Mathematics (2014-today)
Registered students: 182
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Core | Elective Courses | Spring | - | 5 |
| Academic Year | 2021 – 2022 |
| Class Period | Spring |
| Instructors from Other Categories |
|
| Weekly Hours | 3 |
| Class ID | 600187140
|
Course Type 2011-2015
General Foundation
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600187140
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=12901
- Other: http://users.auth.gr/agpapana/TimeSeries/
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
- 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, στατιοναριτυ, autocorrelation function, linear stochastic models: AR (p), MA (q), ARMA (p, q), finding the order of a linear model, non-stationary ARIMA models (p, d, q), methodology of Box & Jenkins, methods of predicting time series.
Educational Material Types
- Notes
- Slide presentations
- Video lectures
- 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 | 2.7 | |||
| Fieldwork | |||||
| Reading Assigment | 40 | 1.3 | |||
| Exams | 30 | 1 | |||
| Total | 150 | 5 |
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)
- Εφαρμοσμένη Στατιστική, Ε. Μπόρα-Σέντα, Π. Μωυσιάδης, Ζήτη, 1990
- Σύγχρονες Μέθοδοι Ανάλυσης Χρονολογικών Σειρών, Σ. Δημέλη, Κριτική, 2013
Last Update
15-03-2020
