Industrial Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Industrial Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Industrial Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 15281834 | Time Series and Forecasting Techniques | 2025 | Spring | 8 | 3+0+0 | 0 | 5 |
| Course Type | Elective |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | Prof. Murat DARÇIN |
| Instructor(s) | - |
| Instructor Assistant(s) | - |
Course Content
Methods of Decomposing Time Series. Linear Trend Function. Smoothing Methods; Simple Moving Averages, Exponential Smoothing Methods, Single Exponential Smoothing, Autoregressive Models and Moving Average Methods. Seasonal Autoregressive Moving Average Methods. Price Indices and Their Importance in Time Series Analysis, Indices, Basic Cycles and Transformations in Price Indices.
Objectives of the Course
Given the basic information needed to make effective predictions.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | X |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P1 | Knowledge of mathematics, natural sciences, fundamental engineering, computational sciences, and industrial engineering-specific subjects; the ability to apply this knowledge to solve complex industrial engineering problems. | 5 |
| P2 | The ability to define, formulate, and analyze complex industrial engineering problems using fundamental science, mathematics, and engineering knowledge, while keeping in mind the relevant UN Sustainable Development Goals. | 5 |
| P4 | The ability to select and utilize appropriate techniques, resources, and modern engineering and information tools, including estimation and modeling, for the analysis and solution of complex industrial engineering problems, while being aware of their limitations. | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Learn the basic concepts of time series analysis. | P.1.39 | 1 |
| O2 | Learn the mathematical foundations of seasonality, trend, and randomness indices. | P.1.40 | 1 |
| O3 | Develop the ability to make effective forecasts under various conditions. | P.1.41 | 1 |
| O4 | Learn the application areas of time series analysis. | P.1.42 | 1 |
| O5 | Learn the basic concepts of time series analysis. | P.2.65 | 1 |
| O6 | Learn the mathematical foundations of seasonality, trend, and randomness indices. | P.2.66 | 1 |
| O7 | Develop the ability to make effective forecasts under various conditions. | P.4.29 | 1 |
| O8 | Learn the application areas of time series analysis. | P.4.30 | 1 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Fundamental concept and introduction to time series, The component of time series, Moving Average Mean, Least Square Method, Linear Trend Function |
| 2 | Quadratic Trend Function, Exponential Trend Function, Determining the Trend Function, Determining the seasonality |
| 3 | Seasonality Index, Using Seasonality Index, Determining effects of Cyclical Movements and Random Factors |
| 4 | Smoothing Methods: Single Moving Averages, Exponential Smoothing Methods |
| 5 | Simple Exponential Smoothing, Linear Moving Average, Linear Exponential Smoothing Methods |
| 6 | Nonlinear Exponential Smoothing, Other Exponential Smoothing Methods, Autoregressive Models and Method of Moving Average |
| 7 | Autocorrelation Coefficients, Calculating Autocorrelation Coefficients and Distribution, Autocorrelation analysis |
| 8 | Midterm |
| 9 | Autocorrelation Tests, Partial Autocorrelation Coefficients and Tests of Autocorrelation Coefficients, Autoregressive Models, Simple and Multiple Regression Analysis |
| 10 | Using Autoregressive Models |
| 11 | Moving Average Methods, Moving Average Models, Prediction of Parameters in Moving Average Models |
| 12 | Box-Jenkins Method |
| 13 | Researching feasibility of the Model, Using model for predictions, Seasonal Autoregressive Moving Average Methods |
| 14 | Prices Indexes and its Importance in Time Series Analysis |
| 15 | Final Exam Preparation |
Textbook or Material
| Resources | Introduction to Time Series Analysis and Forecasting (Wiley Series in Probability and Statistics) 2nd Edition by Douglas C. Montgomery, Cheryl L. Jennings , Murat Kulahci |
| Modern Zaman Serileri ve Yöntemleri, NOBEL AKADEMİK YAYINCILIK, Dr. Üyesi Ebrucan İslamoğlu |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Field Study | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Seminar | - | - |
| Quiz | - | - |
| Listening | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 3 | 42 |
| Midterms | 1 | 30 | 30 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 36 | 36 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 150 | ||
| Total Work Load / 30 | 5 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P1 | P2 | P4 |
|---|---|---|---|---|
| O1 | Learn the basic concepts of time series analysis. | 5 | - | - |
| O2 | Learn the mathematical foundations of seasonality, trend, and randomness indices. | 5 | - | - |
| O3 | Develop the ability to make effective forecasts under various conditions. | 5 | - | - |
| O4 | Learn the application areas of time series analysis. | 5 | - | - |
| O5 | Learn the basic concepts of time series analysis. | - | 5 | - |
| O6 | Learn the mathematical foundations of seasonality, trend, and randomness indices. | - | 5 | - |
| O7 | Develop the ability to make effective forecasts under various conditions. | - | - | 5 |
| O8 | Learn the application areas of time series analysis. | - | - | 5 |