COURSE UNIT TITLE

: LINEAR TIME SERIES ANALYSIS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 5105 LINEAR TIME SERIES ANALYSIS ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR ESIN FIRUZAN

Offered to

Statistics (English)
STATISTICS (ENGLISH)
Statistics (English)

Course Objective

This course provides the theoretical and application parts of time series techniques. Univariate stationary and nonstationary models will be examined in this course. These models will be handled with statistical estimation and inference background. This course will make the students gain experience in identifying systematic pattern of time series data. They should make predictions based on historical values. Students should apply the techniques which they have learned in this course for making decision any forecasts and long term plans.

Learning Outcomes of the Course Unit

1   To distinguish time series components and to develop the skills needed to do empirical research in fields operating with time series data sets,
2   To obtain autocovariance function of any stochastic process,
3   To identify Nonseasonal Box-Jenkins models using autocorrelation and partial autocorrelation function,
4   To test significance of parameter estimates of tentatively identified ARIMA (p,d,q) models,
5   To estimate the parameters with maximum likelihood methods, Yule-walker estimation methods and Least Square estimation methods,
6   To make decisions whether models are adequate,
7   To distinguish between nonseasonal and seasonal models,
8   Developing forecasts based on appropriate ARIMA(p,d,q)x(P,D,Q) models,
9   To make forecast using algorithm.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 1.Hafta Stationarity concepts in time series
2 2.Hafta Zero-mean models/ White Noise/Random Walk Processes
3 3.Hafta Detection of time series components with data scanning techniques
4 4.Hafta Time Series Regression and Exponential Smoothing Techn
5 5.Hafta Autocorrelation Function & Partial Autocorrelation Function
6 6.Hafta Tentatively identification of Nonseasonal Box-Jenkins Models
7 7.Hafta Autoregressive Model AR(p)-Moving Average MA(q)
8 8.Hafta Mixed Autoregressive Moving Average Model ARMA(p,q)
9 9.Hafta Yule-Walker/ /Max.Likelihood/Least Square Estimation
10 10.Hafta Assignments 1/ 2/ 3, Burgs Algthm/Innovation Algrthm
11 11.Hafta Diagnostic Checking
12 12.Hafta The FPE Criterion-The AICC Criterion
13 13.Hafta Forecasting
14 14.Hafta Combining Forecasting, Measuring performance of forecasting

Recomended or Required Reading

*Wei, W.W.S., 2006, Time Series Analysis, Univariate and Multivariate Methods, 2nd EdnPearson
*P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting, 2nd Edn, Prentice-Hall, 2003.
Supplementary Book(s):
*Bowerman L. B., O Connell R. T. (1993) Forecasting and Time Series, 3rd Edition, Duxbury
*Rob J Hyndman, George Athanasopoulos, (2018), Forecasting: Principles and Practice, OText

Planned Learning Activities and Teaching Methods

Lecture, project and presentation.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG 1 ASSIGNMENT 1
2 ASG 2 ASSIGNMENT 2
3 ASG 3 ASSIGNMENT 3
4 FIN FINAL EXAM
5 FCG FINAL COURSE GRADE ASG 1 + ASG 2 + ASG 3/3 * 0.40 + FIN * 0.60
6 RST RESIT
7 FCGR FINAL COURSE GRADE (RESIT) ASG 1 + ASG 2 + ASG 3/3 * 0.40 + RST * 0.60


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of homeworks, assignments and final exam

Language of Instruction

English

Course Policies and Rules

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 51

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 14 3 42
Preparation for final exam 1 36 36
Preparing assignments 3 24 72
Final 1 8 8
TOTAL WORKLOAD (hours) 200

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1555545
LO.2555
LO.355
LO.4455
LO.555
LO.654555553
LO.75555
LO.85555
LO.95555535

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

HOME / ABOUT US / DEGREE PROGRAMMES / GENERAL INFORMATION FOR STUDENTS / INTERNATIONAL RELATIONS