ISB411 Simulation and Modeling

5 ECTS - 3-0 Duration (T+A)- 7. Semester- 3 National Credit

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
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 Öğretim Yöntemleri:
Anlatım
2 Autocorrelation and partial autocorrelation functions Source reading Öğretim Yöntemleri:
Anlatım
3 Examination of stationary Source reading Öğretim Yöntemleri:
Anlatım
4 Portmanteau tests, the index numbers Source reading Öğretim Yöntemleri:
Anlatım
5 Decomposition methods Source reading Öğretim Yöntemleri:
Anlatım
6 Introduction to time series regression analysis, normality tests, the problem of heteroscedasticity Source reading Öğretim Yöntemleri:
Anlatım
7 autocorrelation test, regression analysis in non-seasonal time series Source reading Öğretim Yöntemleri:
Anlatım
8 Mid-Term Exam Review the topics discussed in the lecture notes and sources Ölçme Yöntemleri:
Yazılı Sınav
9 Regression analysis in seasonal time series Source reading Öğretim Yöntemleri:
Anlatım
10 Regression analysis in seasonal tiem series (s, exponential, cubic regression models) Source reading Öğretim Yöntemleri:
Anlatım
11 Exponential smoothing methods Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
12 Autoregression (AR) models and properties Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
13 Moving average (MA) models and properties Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
14 ARIMA models, parameter estimation Source reading Öğretim Yöntemleri:
Anlatım
15 Dickey-Fuller unit root test Source reading Öğretim Yöntemleri:
Anlatım
16 Topic repetitive problem solving Review the topics discussed in the lecture notes and sources Öğretim Yöntemleri:
Alıştırma ve Uygulama
17 Term Exams Review the topics discussed in the lecture notes and sources Ölçme Yöntemleri:
Yazılı Sınav


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

Update Time: 12.06.2024 11:49