GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
FORECASTİNG METHODS/5351303
Course Title: FORECASTİNG METHODS
Credits 3 ECTS 7.5
Course Semester 1 Type of The Course Elective
COURSE INFORMATION
 -- (CATALOG CONTENT)
 -- (TEXTBOOK)
 -- (SUPPLEMENTARY TEXTBOOK)
 -- (PREREQUISITES AND CO-REQUISITES)
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- COURSE OBJECTIVES
 -- COURSE LEARNING OUTCOMES
To have knowledge culture analysis of time series and forecasting
To be capable of analyzing and modeling a time series of
Package programs to apply time series topics
Solving a problem or project,is capable of use in preparing the information
Similar problems in other courses disciplines of analysis evaluation of the ability of
 -- MODE OF DELIVERY
  The mode of delivery of this course is Face to face
 --WEEKLY SCHEDULE
1. Week  An overview of forecasting univariate time series . What can be forecast? . Determining what to forecast . Forecasting data and methods . Some cas
2. Week  Forecasting Tools . Graphics . Numerical data summaries . Some simple forecasting methods . Evaluating forecast accuracy
3. Week  Autocorrelation and seasonality
4. Week  White noise and time series decomposition . Time series components . Moving averages . Classical decomposition . X-12-ARIMA decomposition . Seas
5. Week  Exponential smoothing methods . Simple exponential smoothing . Holt's linear trend method . Exponential trend method . Damped trend methods
6. Week  Error-Trend-Seasonal(ETS) models . Holt-Winters seasonal method . A taxonomy of exponential smoothing methods . Innovations state space models for
7. Week  Midterm exam
8. Week  Brown’s general exponential smoothing method
9. Week  Harmonic model ve Fourier analysis of time series dat
10. Week  Transformations and adjustments
11. Week  Stationarity and differencing
12. Week  Autoregressive models
13. Week  Moving average models
14. Week  Estimation and order selection in ARIMA modelling
15. Week  Seasonal ARIMA models
16. Week  Final Exam
 -- TEACHING and LEARNING METHODS
 -- ASSESSMENT CRITERIA
 
Quantity
Total Weighting (%)
 Midterm Exams
1
20
 Assignment
7
20
 Application
0
0
 Projects
1
30
 Practice
0
0
 Quiz
0
0
 Percent of In-term Studies  
40
 Percentage of Final Exam to Total Score  
60
 -- WORKLOAD
 Activity  Total Number of Weeks  Duration (weekly hour)  Total Period Work Load
 Weekly Theoretical Course Hours
14
3
42
 Weekly Tutorial Hours
0
0
0
 Reading Tasks
14
4
56
 Searching in Internet and Library
14
2
28
 Material Design and Implementation
7
4
28
 Report Preparing
1
14
14
 Preparing a Presentation
0
 Presentation
0
 Midterm Exam and Preperation for Midterm Exam
1
10
10
 Final Exam and Preperation for Final Exam
1
10
10
 Other (should be emphasized)
0
 TOTAL WORKLOAD: 
188
 TOTAL WORKLOAD / 25: 
7.52
 Course Credit (ECTS): 
7.5
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
11. Based on the capabilities of undergraduate level, the students enrolled to the program can develop and deepen their knowledge and skill at the level of expertise on the same field of the undergradute study or a different field.X
22. The students use their theoretical and practical knowledge at the level of expertise in the area of statistics.X
33. The students should evaluate their acquired knowledge and skills in a critical perspective and the critical point of view guides their learning process.X
44. Theoretical and practical knowledge gained in graduate level in the field of Statistics should be applied and transfer to the current problems.X
55. By performing the process from the identification of the scientific research problem to reporting and the process should be transferred in oral, written and visual ways.X
66. The students should use computer software and information technologies on the level required by the field of Statistics.X
77. The students should have the ability to use Statistics in interdisciplinary studies.X
88. The students should have enough foreign language level to pursue statistical literature.X
99. At the required level of field of statistics, he/she should use statistical software and information technology efficiently in a such a way that helps solving problems in his/her research.X
1010. In the process of applying knowledge in a professional sense, social, scientific, and ethical values should be regarded.X
 -- NAME OF LECTURER(S)
   (Prof.Dr.Reşat Kasap)
 -- WEB SITE(S) OF LECTURER(S)
   (https://abs.gazi.edu.tr/rkasap)
 -- EMAIL(S) OF LECTURER(S)
   (rkasap@gazi.edu.tr)