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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
ENM 526TIME MANAGEMENT ANALYSIS AND FORECASTING TECHNIQUES3 + 02nd Semester7,5

COURSE DESCRIPTION
Course Level Doctorate Degree
Course Type Elective
Course Objective The aim of this course is to give information about time series techniques and applications of time series analysis.
Course Content Introduction to time series analysis, basic concepts, autocovariation and autocorrelation, stationary and non-stationary time series, Box-Jenkins stochastic process models, ARMA and ARIMA models, model determination, parameter estimation, goodness-of-fit test, forecast with Box-Jenkins stochastic process models, non-linear models.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Student can gain the ability of recognizing the distinguished properties of time series analysis.
2Student can use the methods proposed for time series analysis.
3Student can solve seasonal and yearly time series and make forecasts for medium term.

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10
LO 0014422322212
LO 0025422322213
LO 0034422422212
Sub Total1312661066637
Contribution4422322212

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)13113
Assignments5420
Mid-terms13535
Final examination14545
Report / Project14040
Total Work Load

ECTS Credit of the Course






195

7,5
COURSE DETAILS
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L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes