COURSE UNIT TITLE

: TIME SERIES REGRESSION

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 3138 TIME SERIES REGRESSION ELECTIVE 3 0 0 5

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR ESIN FIRUZAN

Offered to

Statistics
Statistics(Evening)

Course Objective

To introduce the student forecasting using different smoothing methods and time series decomposition methods.

Learning Outcomes of the Course Unit

1   To distinguish time series components
2   To develop trend models
3   To apply the decomposition methods
4   To apply the exponential smoothing methods
5   Developing forecasts based on appropriate forecasting models

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Forecasting and Time Series
2 Time Series and its Components
3 Modeling trend by using trend functions (linear, quadratic, cubic etc.)
4 Assumptions of Regression (Normality, Heteroskedaticity, autocorrelation
5 Additive Decomposition Methods
6 Multiplicative Decomposition
7 Testing of Seasonality
8 Kruskall Wallis, ADF Testi, Jarque Berra, Shapiro-Wilk Test
9 Regression analysis for nonseasonal time series
10 Regression analysis for seasonal time series
11 Simple Exponential Smoothing
12 Holts Exponential Smoothing
13 Winters Exponential Smoothing
14 Prediction and Prediction Interval

Recomended or Required Reading

Textbook(s):
Bowerman L. B., O Connell R. T. (1993) Forecasting and Time Series, 3rd Edition, Duxbury
Supplementary Book(s):
1. Hanke J. E., Wichern D. W. (2008) Business Forecasting, 9th Edition, Pearson

Planned Learning Activities and Teaching Methods

Lecture format, built around the textbook readings and computer applications with numerous examples chosen to illustrate theoretical concepts. Lots of drill with emphasis on practice. Questions are encouraged and discussion of material stressed.
Lecture, project and presentation.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FINS FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 + ASG * 0.20 + FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.20 + RST * 0.50


Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of project, presentation and exams

Language of Instruction

Turkish

Course Policies and Rules

Student responsibilities:
Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy. You can find the undergraduate policy at http://web.deu.edu.tr/fen

Contact Details for the Lecturer(s)

DEU Fen Fakültesi Istatistik Bölümü
e-mail: esin.firuzan@deu.edu.tr
Tel: 0232 301 85 57

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 12 1 12
Preparation for midterm exam 1 21 21
Preparation for final exam 1 28 28
Preparing assignments 1 12 12
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 116

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.155453
LO.2555
LO.355453
LO.4553
LO.55553

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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