TÜRKÇE
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Information
| Code | ISB411 |
| Name | Simulation and Modeling |
| Term | 2024-2025 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 | Lisans Dersi |
| Type | Normal |
| Label | FE Field Education Courses E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. MAHMUDE REVAN ÖZKALE |
| Course Instructor |
Prof. Dr. MAHMUDE REVAN ÖZKALE
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
THe aim of this course is time series modeling, forecasting and prediction, and the use of a variety of package programs related to them
Course Content
This course covers the components of the time series, the time series graphics, the decomposition methods, the regression models in time series, exponential smoothing techniques, Box-Jenkins Models, the statistical package programs
Course Precondition
none
Resources
1. Kadılar, C., Öncel Çekim, H. (2020), SPSS ve R Uygulamalı Zaman Serileri Analizine Giriş. Seçkin Yayıncılık, Ankara 2. Cryer, J. D. (1986), Time Series Analysis. PWS-KENT Publishing Company
Notes
Sevüktekin, M., Nargeleçekenler, M. (2005), Zaman Serileri Analizi. Nobel Yayın Dağıtım
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Distinguish the components of the time series |
| LO02 | Comment the time series graphics |
| LO03 | Apply the decomposition methods |
| LO04 | Determine the regression model that fits the data |
| LO05 | Distinguish the difference between smoothing techniques |
| LO06 | Explain the statistical basics of Box Jenkins models |
| LO07 | Distinguish between the Box Jenkins models that fit the time series data |
| LO08 | Apply the necessary methods for time-series forecasting and prediction |
| LO09 | Use the statistical package programs necessary for time series analysis |
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 | 4 |
| 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 | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Utilize computer programs and builds models, solves problems, does analyses and comments about problems concerning randomization | |
| 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 | |
| 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 | 4 |
| PLO13 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Have capability on effective and productive work in a group and individually | 2 |
| 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 | 1 |
| 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 | Interpretation of time series and time-series graphics | Source reading | |
| 2 | Autocorrelation and partial autocorrelation functions | Source reading | |
| 3 | Examination of stationary | Source reading | |
| 4 | Portmanteau tests, the index numbers | Source reading | |
| 5 | Decomposition methods | Source reading | |
| 6 | Introduction to time series regression analysis, normality tests, the problem of heteroscedasticity | Source reading | |
| 7 | autocorrelation test, regression analysis in non-seasonal time series | Source reading | |
| 8 | Mid-Term Exam | Review the topics discussed in the lecture notes and sources | |
| 9 | Regression analysis in seasonal time series | Source reading | |
| 10 | Regression analysis in seasonal tiem series (s, exponential, cubic regression models) | Source reading | |
| 11 | Exponential smoothing methods | Source reading | |
| 12 | Autoregression (AR) models and properties | Source reading | |
| 13 | Moving average (MA) models and properties | Source reading | |
| 14 | ARIMA models, parameter estimation | Source reading | |
| 15 | Dickey-Fuller unit root test | Source reading | |
| 16 | Topic repetitive problem solving | Review the topics discussed in the lecture notes and sources | |
| 17 | Term Exams | Review the topics discussed in the lecture notes and sources |
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 | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 18 | 18 |
| Total Workload (Hour) | 114 | ||
| Total Workload / 25 (h) | 4,56 | ||
| ECTS | 5 ECTS | ||