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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
EKNM 315NONLINEAR TIME SERIES ANALYSIS3 + 05th Semester5

COURSE DESCRIPTION
Course Level Bachelor's Degree
Course Type Elective
Course Objective Data acquisition, classification and properties of functions used to determine the Nonlinear Time-series (NLTS). To learn the use of models for the system to be controlled and predicted.
Course Content Week Subject 1 Notation and terminology 2 Classification of NLTS data 3 ARCH model 4 Threshold model 5 Non parametric autoregressive model 6 Local linear modelling 7 Spline approximation 8 Mid-term 9 Threshold autoregressive model 10 GARCH model 11 Asymptothic properties of estimators 12 MLE and Bootstrap 13 Test of the ARCH effect 14 Stochastic volatility models
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
2To be able to read NLTS graphs.
1To be able to explain the features of NLTS data.
3To be able to interpret various functions of NLTS data.
4To be able to model NLTS data mathematically.
5To be able to make predictions based on NLTS data.
6To be able to measure the reliability of NLTS analyses.

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12
LO 001121221343523
LO 002213455242321
LO 003223214521222
LO 004231254235421
LO 005231245232542
LO 006325423542321
Sub Total121314161922192015221410
Contribution222334333422

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)14456
Mid-terms11515
Final examination11717
Total Work Load

ECTS Credit of the Course






130

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