| Semester | Course Unit Code | Course Unit Title | T+P+L | Credit | Number of ECTS Credits | Last Updated Date |
| 1 | ECE547 | TIME SERIES DATA ANALYSIS | 3+0+0 | 3 | 7,5 | 14.05.2025 |
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Language of Instruction
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English
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Level of Course Unit
|
Master's Degree
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|
Department / Program
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ELECTRICAL AND COMPUTER ENGINEERING
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Type of Program
|
Formal Education
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Type of Course Unit
|
Elective
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Course Delivery Method
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Face To Face
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Objectives of the Course
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1-Perform time-series analysis in Python and interpret the results based on the given data.
2-Identify and justify the need to normalize data when comparing different time series.
3-Identify special types of time series
4-Analyze stationarity and its existence
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Course Content
|
A time series is a series of data points ordered over time. Time series forecasting is conceptual modeling for predicting future events based on previously known events. Time series analysis creates a time-oriented projection using meaningful statistical analyzes on the time series. This course provides an opportunity to introduce the different properties of time series and how we can model them to obtain accurate predictions.
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Course Methods and Techniques
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|
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Prerequisites and co-requisities
|
None
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Course Coordinator
|
Associate Prof.Dr. Zafer Aydın zafer.aydin@agu.edu.tr
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Name of Lecturers
|
Asist Prof.Dr. Mehmet Gökhan Bakal gokhan.bakal@agu.edu.tr
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Assistants
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None
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Work Placement(s)
|
No
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Recommended or Required Reading
|
Resources
|
Relevant Video Resources
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Course Category
|
Mathematics and Basic Sciences
|
%10
|
|
|
Engineering
|
%60
|
|
|
Engineering Design
|
%30
|
|
|
Social Sciences
|
%0
|
|
|
Education
|
%0
|
|
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Science
|
%0
|
|
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Health
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%0
|
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Field
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%0
|
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Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
|
In-Term Studies
|
|
Quiz/Küçük Sınav
|
5
|
%
25
|
|
Ödev
|
5
|
%
25
|
|
Proje/Çizim
|
1
|
%
40
|
|
Sunum/Seminer
|
1
|
%
10
|
|
Total
|
12
|
%
100
|
ECTS Allocated Based on Student Workload
|
Activities
|
Total Work Load
|
|
Ev Ödevi
|
5
|
2
|
10
|
|
Sunum için Hazırlık
|
1
|
3
|
3
|
|
Sunum
|
1
|
1
|
1
|
|
Proje
|
1
|
40
|
40
|
|
Kısa Sınav
|
5
|
1
|
5
|
|
Okuma
|
7
|
1
|
7
|
|
Rapor
|
1
|
30
|
30
|
|
Araştırma
|
9
|
2
|
18
|
|
Yüz Yüze Ders
|
14
|
3
|
42
|
|
Asenkron Ders
|
10
|
2
|
20
|
|
Derse Devam
|
14
|
3
|
42
|
|
Final Sınavı
|
1
|
2
|
2
|
|
Total Work Load
| |
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Number of ECTS Credits 7,5
220
|
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
| No | Learning Outcomes |
|
1
| Explain the fundamental assumptions of time series data and how to examine them?? |
|
2
| Perform scientific coding in Python and make statistical analysis? |
|
3
| Evaluate models for time-series analysis by analyzing the role of residuals in model selection |
|
4
| Predict future trends based on patterns observed in historical time-series data |
Weekly Detailed Course Contents
| Week | Topics | Study Materials | Materials |
| 1 |
Intro to Time Series Analysis
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| 2 |
Time Series as Supervised Learning, Data preparation
|
|
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| 3 |
Basic Feature Engineering, Resampling and Interpolation?
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| 4 |
Power Transforms, Moving Average Smoothing
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|
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| 5 |
Decomposing Time Series Data
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|
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| 6 |
Use and Remove Trends?and Seasonality?
|
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| 7 |
Forecasting Performance Measures?
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| 8 |
Persistence Model for Forecasting?
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Contribution of Learning Outcomes to Programme Outcomes
Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant
https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77736&lang=en