• HOME
• ABOUT US
  • Name Address
  • General Description
  • Governance
  • Academic Calendar
  • Degree Programmes
  • List of Programmes in English
  • General Admission and Registration Procedures
  • Credit Mobility and Recognition of Prior Learning
  • ECTS Credit Allocation
  • Academic Guidance
  • Contacts
• Degree Programmes
  • Third Cycle Programmes (Doctorate Degree)
  • Second Cycle Programmes (Master's Degree)
  • First Cycle Programmes (Bachelor's Degree)
  • Short Cycle Programmes (Associate's Degree)
• GENERAL INFORMATION FOR STUDENTS
  • Cost of Living
  • Accommodation
  • Meals
  • Medical Facilities
  • Facilities for Disabled Students
  • Insurance
  • Financial Supports
  • Student Affairs Office
  • Practical Information for International Students
  • Language Courses
  • Work Placement Possibilities
  • Sports, Social and Cultural Facilities
  • Student Associations and Clubs
  • About İzmir - Turkey
• INTERNATIONAL RELATIONS
  • International Relations Office
  • Registration Procedures
  • Agreements
  • Coordinators
  • Documents
  • List of Programmes in English
  • ECTS / DS Labels

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS VYA 5024 TIME SERIES AND FORECASTING TECHNIQUES ELECTIVE 3 0 0 4

Offered By

DATA MANAGEMENT AND ANALYSIS

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR EMRAH GÜLAY

Offered to

DATA MANAGEMENT AND ANALYSIS

Course Objective

Introducing the characteristics of time series data and the use of forecasting methods.

Learning Outcomes of the Course Unit

1   1. To be able to understand the visual analysis of time series data 2   2. To be able to classify and organize these data 3   3. To be able to use the calculation methods adopted on these data 4   4. To be able to summarize these data correctly 5   5. To be able to use forecasting methods based on time series data correctly

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introdcution to time series 2 The components of time series data and Moving Average Method 3 Stationarity 4 Examining the stationarity of a time series 5 AR and MA models 6 ARMA, ARIMA and seasonal ARIMA models 7 The applications of using R to decompse time series data 8 The applications of using R to model time series data 9 Midterm 10 Holt-Winters model 11 ETS and TBATS models 12 Introduction to Hybrid models I 13 Introduction to Hybrid models II 14 The concepts of rolling and expanding windows in forecasting 15 The applications of using R

Recomended or Required Reading

1. Statistics and Data Analysis for Financial Engineering (Springer Texts in Statistics) David Ruppert, 2010. 2. Analysis of Financial Data by Gary Koop ISBN 978-0-470-01321-2 November 2005, ©2006 Paperback.

Planned Learning Activities and Teaching Methods

1- Lecture Method, 2- Demonstration Method with Applications, 3-Determined Cases Discussed Analysis Method

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 STT TERM WORK (SEMESTER) 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.30 + STT * 0.30 + FIN* 0.40 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.30 + RST* 0.40

Further Notes About Assessment Methods

None

Assessment Criteria

The weighted average of the midterm grade, the midterm work and the final grade must be 75 and above.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 3 42 Preparations before/after weekly lectures 10 2 20 Preparation for midterm exam 1 10 10 Preparation for final exam 1 10 10 Preparing assignments 5 3 15 Midterm 1 3 3 Final 1 3 3 TOTAL WORKLOAD (hours) 103

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 LO.1 1 1 LO.2 1 1 1 1 1 LO.3 1 LO.4 1 1 1 1 LO.5 1 1 1 1 1 1