1. SECOND CYCLE - MASTER'S DEGREE
  2. THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES
  3. INDUSTRIAL ENGINEERING DEPARTMENT
  4. 1261 Industrial Engineering
Course Information Course Learning Outcomes Course's Contribution To Program ECTS Workload Course Details
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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
ENM 526TIME MANAGEMENT ANALYSIS AND FORECASTING TECHNIQUES3 + 01st Semester7,5

COURSE DESCRIPTION
Course Level Master's 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.

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
Assignments4520
Mid-terms16060
Final examination17373
Total Work Load

ECTS Credit of the Course





195

7,5
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2025-2026 Fall1AHMET ALP ŞENOCAK


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Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester Mode of Delivery
ENM 526 TIME MANAGEMENT ANALYSIS AND FORECASTING TECHNIQUES 3 + 0 1 Turkish 2025-2026 Fall Face to Face
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Asts. Prof. Dr. AHMET ALP ŞENOCAK asenocak@pau.edu.tr MUH A0457 %
Goals The aim of this course is to give information about time series techniques and applications of time series analysis.
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.
Topics
Materials
Materials are not specified.
Resources
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam50Final Exam
Midterm Exam50Midterm Exam
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes