School/Faculty/Institute	Graduate School
Course Code	BDA 543
Course Title in English	Time Series Forecasting
Course Title in Turkish	Zaman Serisi Tahminleme
Language of Instruction	EN
Type of Course	Ters-yüz öğrenme
Level of Course	Orta
Semester	Spring
Contact Hours per Week	Lecture: 3 Recitation: Lab: Other:
Estimated Student Workload	172 hours per semester
Number of Credits	7.5 ECTS
Grading Mode	Standard Letter Grade
Pre-requisites	None
Co-requisites	None
Expected Prior Knowledge	Basic knowledge of • probability distributions, expectation, variance • linear regression, logistic regression
Registration Restrictions	Only Graduate Students
Overall Educational Objective	To learn the basic methods and models for time series forecasting with hands-on applications.
Course Description	This course reviews the fundamental methods and models that are used in time series forecasting. It presents the basic properties of time series data such as stationarity, seasonality and trend. It covers common forecasting methods and models including smoothing methods, ARIMA models, proportional hazard models, GARCH models, VARs. It also introduces various machine learning techniques that are applied in time series forecasting.

Course Learning Outcomes and Competences Upon successful completion of the course, the learner is expected to be able to: 1) Durağanlık, trend, mevsimsellik ve otokorelasyon gibi zaman serisi verilerinin temel kavramlarını anlamak 2) Zaman serisi verilerinin bileşenlerini belirleme ve işlemek 3) Bir zaman serisi tahmin problemini çözmek için uygun yöntem veya modelleri belirlemek 4) Tahmin prosedürünü uygulayın ve sonuçları yorumlamak

Program Learning Outcomes/Course Learning Outcomes	1	2	3	4
1)
2)
3)
4)
5)
6)
7)
8)
9)
10)

Relation to Program Outcomes and Competences

N None	S Supportive	H Highly Related

	Program Outcomes and Competences	Level	Assessed by
1)		N
2)		N
3)		N
4)		N
5)		N
6)		N
7)		N
8)		N
9)		N
10)		N

Prepared by and Date	,
Course Coordinator	ÖZGÜR ÖZLÜK
Semester	Spring
Name of Instructor

Course Contents

Hafta	Konu
1)	• Zaman serisi verilerine giriş • Zaman serisi tahminine giriş • Durağanlık, Mevsimsellik, Trend • Regresyon yöntemlerinin gözden geçirilmesi (eğer zaman kalırsa)
2)	• Zaman serisi verilerine giriş • Zaman serisi tahminine giriş • Durağanlık, Mevsimsellik, Trend • Regresyon yöntemlerinin gözden geçirilmesi (eğer zaman kalırsa)
3)	Düzgünleştirme Yöntemleri
4)	Düzgünleştirme Yöntemleri
5)	• ARMA, ARIMA, SARIMA modelleri • Otokorelasyon
6)	• ARMA, ARIMA, SARIMA modelleri • Otokorelasyon
7)	• Hayatta Kalma Analizi • Kaplan-Meier Eğrisi • Orantılı Tehlike Modelleri
8)	• Hayatta Kalma Analizi • Kaplan-Meier Eğrisi • Orantılı Tehlike Modelleri
9)	• ARCH, GARCH modelleri
10)	ARCH, GARCH modelleri
11)	Var Modelleri
12)	Var Modelleri
13)	• Makine Öğrenimi Uygulamaları • Yapay Sinir Ağları, Boosting yöntemleri vb.
14)	• Makine Öğrenimi Uygulamaları • Yapay Sinir Ağları, Boosting yöntemleri vb.
15)	Proje/Sunum Dönemi
16)	Proje/Sunum Dönemi

Required/Recommended Readings	None
Teaching Methods	Flipped classroom/Exercise/Laboratory/Active learning
Homework and Projects	Students will be given 5 assignments: one assignment each week from week #2 to week #5. Each assignment will include numerical applications of the methods or models that will be taught in class. Students will have one week to submit an assignment.
Laboratory Work	Students will apply the methods they learned using a statistical computation program.
Computer Use	Students will apply the methods they learned using a statistical computation program.
Other Activities	None
Assessment Methods	Assessment Tools Count Weight Ödev 5 % 100 TOTAL % 100
Course Administration	02123953600

ECTS Student Workload Estimation

Activity	No/Weeks	Hours			Calculation
	No/Weeks per Semester	Preparing for the Activity	Spent in the Activity Itself	Completing the Activity Requirements
Ders Saati	14	2	1.5	1	63
Laboratuvar	14	2	1.5	1	63
Ödevler	5	12			60
Total Workload					186
Total Workload/25					7.4
ECTS					7.5