Time Series Analysis and Forecasting
Information Systems Engineering
Faculty of Technology
First Cycle (Bachelor's Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Time Series Analysis and Forecasting | TBL335 | Elective | Bachelor's degree | 3 | Fall | 5 |
Name of Lecturer(s)
Prof. Dr. Halil YİĞİT
Associate Prof. Dr. Zeynep Hilal KİLİMCİ
Assistant Prof. Dr. Önder YAKUT
Learning Outcomes of the Course Unit
1) Understand time series analysis and prediction methods technically
2) Have in-depth knowledge about time series and prediction theory, concepts and techniques
3) Applies models to real data with different programming languages
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | ||
| Learning Outcomes | ||||||||||||
| 1 | Low | No relation | No relation | Low | No relation | No relation | Low | No relation | No relation | No relation | No relation | |
| 2 | No relation | Middle | No relation | No relation | Middle | No relation | No relation | Middle | No relation | Middle | Middle | |
| 3 | No relation | No relation | High | No relation | No relation | High | No relation | No relation | High | No relation | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Statistics and Probability
Course Contents
Zaman serileri teorisinin temel kavramlarını ve analiz yöntemlerini öğretmek, Çoğunlukla zaman serisi verileriyle çalışmak üzere tasarlanan geleneksel zaman serisi analizi yöntemlerini öğrenilmesi, Kesit ve zaman serileri arasındaki farkları ve bu tip verilerle çalışırken ortaya çıkan spesifik ekonomik problemleri anlaşılması. Dikkate alınan yöntemler ve modeller, gerçek ekonomik verilerin ve yazılımların (R, Python) kullanılarak pratik olarak uygulanması.
Weekly Schedule
1) Introduction of time series data, examples and discovery analysis2) R or Python programming language introduction
3) Estimator tool box
4) Autocorelation and seasonality
5) White noise and time series decomposition
6) Exponential correction methods
7) ETS models
8) Mid-Term Exam /Assessment Dec.
9) Conversions and adjustments
10) Stasis and difference
11) Smoothing methods
12) Non-seasonal ARIMA models
13) Seasonal ARIMA models
14) Dynamic regression
15) Advanced methods
16) Project Presentations
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Contribution of Semester Studies to Course Grade |
20% |
|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
||||||||||||
Contribution of Final Examination to Course Grade |
80% |
|||||||||||
Total | 100% | |||||||||||
Language of Instruction
Turkish
Work Placement(s)
Not Required