Course Code	Semester	Course Name	LE/RC/LA	Course Type	Language of Instruction	ECTS
MB0048		Time Series	2/2/0	DE	Turkish	5

Course Goals	Forecasting (predicting future values of the time series variable).
Prerequisite(s)	None
Corequisite(s)	None
Special Requisite(s)	The minimum qualifications that are expected from the students who want to attend the course.(Examples: Foreign language level, attendance, known theoretical pre-qualifications, etc.)
Instructor(s)	Assoc. Prof. S. Hikmet ÇAĞLAR
Course Assistant(s)	Arş. Gör. Tuğba Daymaz
Schedule	Tuesday, 11:00-12:45 Wednesday, 09:00-10:45
Office Hour(s)	Tuesday, 13:00-15:00 via IKU CATS
Teaching Methods and Techniques	Lectures, seminars and lab work.
Principle Sources	Prof.Dr.Neyran Orhunbilge;Zaman Serileri Analizi Tahmin ve Fiyat İndeksleri;İşletme Fakültesi Yayını 1999. Anderson O.D;Time Series Analysis,North Holland Publishing company,Amsterdam,1982.
Other Sources	-

Course Schedules
Week	Contents	Learning Methods
1. Week	Identifying Patterns in Time Series Data	Oral presentation and laboratory
2. Week	Autocorrelations	Oral presentation and laboratory
3. Week	Linear static stochastic models: AR (p) Model	Oral presentation and laboratory
4. Week	Linear static stochastic models: MA (q) Model	Oral presentation and laboratory
5. Week	Linear static stochastic models	Oral presentation and laboratory
6. Week	Linear static stochastic models	Oral presentation and laboratory
7. Week	Unit root tests	Oral presentation and laboratory
8. Week	Midterm Exam	Midterm Exam
9. Week	Unit root tests	Oral presentation and laboratory
10. Week	Cointegration and error correction models	Oral presentation and laboratory
11. Week	Examination of time series models I	Oral presentation and laboratory
12. Week	Examination of time series models II	Oral presentation and laboratory
13. Week	Examination of time series models III	Oral presentation and laboratory
14. Week	Examination of time series models IV	Oral presentation and laboratory
15. Week	Final Exam
16. Week	Final Exam
17. Week	Final Exam

Assessments
Evaluation tools	Quantity	Weight(%)
Midterm(s)	1	40
Final Exam	1	60

Program Outcomes PO-1 Interpreting advanced theoretical and applied knowledge in Mathematics and Computer Science. PO-2 Critiquing and evaluating data by implementing the acquired knowledge and skills in Mathematics and Computer Science. PO-3 Recognizing, describing, and analyzing problems in Mathematics and Computer Science; producing solution proposals based on research and evidence. PO-4 Understanding the operating logic of computer and recognizing computational-based thinking using mathematics as a discipline. PO-5 Collaborating as a team-member, as well as individually, to produce solutions to problems in Mathematics and Computer Science. PO-6 Communicating in a foreign language, and interpreting oral and written communicational abilities in Turkish. PO-7 Using time effectively in inventing solutions by implementing analytical thinking. PO-8 Understanding professional ethics and responsibilities. PO-9 Having the ability to behave independently, to take initiative, and to be creative. PO-10 Understanding the importance of lifelong learning and developing professional skills continuously. PO-11 Using professional knowledge for the benefit of the society.

Learning Outcomes LO-1 test for unit roots in univariate time series LO-2 analyse the relationships between multiple, stationary time series LO-3 Apply basic time series techniques and various forecasting models to data. LO-4 Evaluate the forecast accuracy performance of forecasting models. LO-5 Understand the definitions of the important stochastic processes used in time series modelling, and the properties of those models. LO-6 Appreciate the important features that describe a time series, and perform simple analyses and computations on series.

Course Assessment Matrix:

	PO 1	PO 2	PO 3	PO 4	PO 5	PO 6	PO 7	PO 8	PO 9	PO 10	PO 11
LO 1
LO 2
LO 3
LO 4
LO 5
LO 6