COURSE UNIT TITLE

: TIME SERIES MODELS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
FPE 0055 TIME SERIES MODELS ELECTIVE 4 0 0 6

Offered By

Faculty Of Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR BURCU HÜDAVERDI

Offered to

Biology
Chemistry
Computer Science
Statistics
Mathematics
Physics
Faculty Of Science

Course Objective

The principal objective of the course is to give fundamental knowledge of serially dependent observations, mathematical models for such data as well as introduction and use of tools to identify and estimate such models. The course will cover time series decomposition, smoothing techniques, linear time series models; stationary ARMA and multiplicative SARMA classes, non-stationary ARIMA and multiplicative SARIMA classes. The students will have the knowledge in identifying systematic pattern of time series data and also they can apply the techniques which they have learned in this course for forecasting and long term plans. The students will use R, Minitab, SPSS statistical packages for computation, visualization, and analysis of time series data.

Learning Outcomes of the Course Unit

1   To learn the concept of time series or serially correlated observations.
2   To learn how to define and decompose time series components.
3   To learn smoothing techniques for times series data
4   To be able to estimate mean, variance, correlation and partial correlation functions of a time series as a realization of stochastic process
5   To identify stationary ARMA and multiplicative SARMA classes, non-stationary ARIMA and multiplicative SARIMA classes
6   To test significance and adequacy of the time series model
7   To be able to forecast using the adequate time series model through Box-Jenkins methodology

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Characteristics of Time Series Data, stationarity/nonstationarity.
2 Components of Time series
3 Smoothing techniques: Exponential, Double Exponential Methods, Winter s Method
4 Estimating mean, variance, correlation and partial correlation functions of a time series
5 Estimating mean, variance, correlation and partial correlation functions of a time series
6 Correlogram, Autocorrelation Function, Partial Autocorrelation Function
7 Identification Nonseasonal Autoregressive-Moving Average Model ARMA(p,q)
8 Estimation, Diagnostic Checking,Forecasting ARMA(p,q) Models
9 Project Presentation 1
10 Midterm
11 Identification Seasonal Autoregressive-Moving Average Model, SARIMA
12 Estimation, Diagnostic Checking , Forecasting Seasonal ARIMA Models
13 Improving the time series model
14 Project Presentation 2

Recomended or Required Reading

Wei, W.W.S., 2006, Time Series Analysis, Univariate and Multivariate Methods, 2nd Edn. Pearson
Bowerman L. B., O Connell R. T. 1993 Forecasting and Time Series, 3rd Edition, Duxbury
Jonathan D. Cryer, Kung-Sik Chan, (2008) Time Series Analysis with Application in R, Second Edition, Springer.
Paul S.P. Cowpertwait , Andrew V. Metcalfe (2009), Introductory Time Series with R, Springer.

Materials:Lecture Notes

Planned Learning Activities and Teaching Methods

Lecture format, built around the textbook readings and R/SSPSS/Minitab statistical package applications with examples chosen to illustrate theoretical concepts. Applications and examples. Questions and discussion are encouraged. Lecture, project and presentation.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 PRJ PROJECT
3 FIN FINAL EXAM
4 FCGR FINAL COURSE GRADE (RESIT) VZ * 0.30 + PRJ * 0.30 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) VZ * 0.30 + PRJ * 0.30 + RST * 0.40


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

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of project and exams

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.

Contact Details for the Lecturer(s)

Prof. Dr. Burcu Hüdaverdi
e-mail: burcu.hudaverdi@deu.edu.tr
tel: +90-232-3018597

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 2 26
Tutorials 13 1 13
Preparations before/after weekly lectures 11 2 22
Preparation for midterm exam 1 15 15
Preparation for final exam 1 30 30
Preparing presentations 2 25 50
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 160

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.133
LO.2453
LO.324
LO.422343
LO.53343
LO.6334
LO.733

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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