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Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| STATISTICS PR. | |
| Code | ISB417 |
| Name | Time Series Analysis with Python |
| Term | 2026-2027 Academic Year |
| Semester | 7. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. MAHMUDE REVAN ÖZKALE ATICIOĞLU |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim of this course is to familiarize students with the structure and dynamics of time series data, teach them statistical analysis and modeling techniques for this data, and equip them with forecasting and analysis skills for real-world problems using the Python programming language.
Course Content
This course covers stationarity analysis, decomposition methods, regression approaches in time series, seasonal and non-seasonal time series models, and machine learning and deep learning techniques in time series, all with Python applications.
Course Precondition
none
Resources
Peixeiro, M. 2022. Time Series Forecasting in Python. Manning Publications Kadılar, C., Öncel Çekim, H. 2020. SPSS ve R uygulamalı Zaman Serileri Analizine Giriş. Seçkin Yayıncılık, Ankara
Notes
Smith, J. 2019. Time Series with Python. Jim Smith Publishing
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explains and interprets the basic characteristics of time series data (trend, seasonality, stationarity, autocorrelation). |
| LO02 | Applies the basic statistical concepts and methods used in time series analysis. |
| LO03 | Processes, visualizes, and analyzes time series data using the Python programming language. |
| LO04 | Establishes and compares classic time series models such as AR, MA, ARMA, and ARIMA. |
| LO05 | Determines the most suitable model by using model selection criteria (AIC, BIC, etc.). |
| LO06 | Perform stationarity tests (e.g., ADF test) and evaluate the results. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explain the essence fundamentals and concepts in the field of Statistics | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Emphasize the importance of Statistics in life | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Define basic principles and concepts in the field of Law and Economics | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Produce numeric and statistical solutions in order to overcome the problems | |
| PLO05 | Bilgi - Kuramsal, Olgusal | Use proper methods and techniques to gather and/or to arrange the data | 5 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Utilize computer programs and builds models, solves problems, does analyses and comments about problems concerning randomization | 5 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Apply the statistical analyze methods | 4 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Make statistical inference (estimation, hypothesis tests etc.) | |
| PLO09 | Bilgi - Kuramsal, Olgusal | Generate solutions for the problems in other disciplines by using statistical techniques and gain insight | 5 |
| PLO10 | Bilgi - Kuramsal, Olgusal | Discover the visual, database and web programming techniques and posses the ability of writing programs | |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Distinguish the difference between the statistical methods | |
| PLO12 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Make oral and visual presentation for the results of statistical methods | |
| PLO13 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Have capability on effective and productive work in a group and individually | |
| PLO14 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs | |
| PLO15 | Yetkinlikler - Öğrenme Yetkinliği | Develop scientific and ethical values in the fields of statistics-and scientific data collection |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Time series data, time series components, number of lags, autocorrelation and partial autocorrelation functions, difference operations, stationarity, white noise series. | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 2 | Preprocessing and visualization time series data in python | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 3 | Decomposition Methods and Python Applications | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 4 | Exponential smoothing methods and Python implementations | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 5 | Practical and theoretical problem solving. | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 6 | Stability tests (ADF test) and real data applications | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 7 | Regression analysis in time series-testing assumptions and Python implementation on real data | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Midterm Exam | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Regression analysis of non-seasonal time series | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 10 | Model selection criteria | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 11 | Non seasonal Box-Jenkins models | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 12 | Seasonal Box-Jenkins models | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 13 | Machine learning for times series | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 14 | Use deep learning for time series | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 15 | Time series forecasting with Python programming. | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 16 | Term Exams | Final Exam | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Final Exam |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 15 | 15 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 10 | 10 |
| Final Exam | 1 | 15 | 15 |
| Total Workload (Hour) | 124 | ||
| Total Workload / 25 (h) | 4,96 | ||
| ECTS | 5 ECTS | ||