COURSE UNIT TITLE

: APPLIED TIME SERIES ANALYSIS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
DSM 5012 APPLIED 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 BURCU HÜDAVERDI AKTAŞ

Offered to

Data Science
Data Science (Non-Thesis-Evening)

Course Objective

The course will provide a basic introduction to modern time series analysis. The main aim of the course is to develop the skills necessary to conduct empirical research in the main areas subject to time series data. Within the scope of the course, the latest developments in time series analysis will be followed and ongoing research areas will be presented. Students will be able to apply the approaches they learn in this course for forecasting and long-term planning. During the course, the R-Project will be used for investigating time series data structure, modeling, visualization and analysis of time series data.

Learning Outcomes of the Course Unit

1   To learn characteristics of time series data
2   To learn stationary and non-stationary time series models
3   To be able to use autocorrelation and partial autocorrelation functions
4   To be able to model time series (description, parameter estimation and model selection)
5   To be able to model seasonal time series (description, parameter estimation and model selection)
6   Testing the significance and adequacy of the time series model
7   To be able to forecast using the adequate time series model

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, Winters Method
4 Correlogram, Autocorrelation Function, Partial Autocorrelation Function
5 Identification Nonseasonal Autoregressive-Moving Average Model ARMA(p,q)/ARIMA(p,d,q)
6 Estimation, Diagnostic Checking,Forecasting ARMA(p,q)/ARIMA(p,d,q) Models
7 Project Presentation 1
8 Identification Seasonal Autoregressive-Moving Average Model, SARIMA(p,q)(P,D,Q)
9 Estimation, Diagnostic Checking , Forecasting Seasonal ARIMA(p,q)(P,D,Q) Models
10 Modelling volatility with ARCH-GARCH Models
11 Artificial Neural Networks and Time Seties Modeling
12 Long-Short term memory (LSTM) in Articial neural Network (ANN)
13 Support Vector machine (SVM) in time series modeling
14 Project Presentation 2

Recomended or Required Reading

Textbooks:
Paul S.P. Cowpertwait · Andrew V. Metcalfe (2009), Introductory Time Series with R, Springer
Jonathan D. Cryer  Kung-Sik Chan (2008), Time Series Analysis with Application in R (SEcond Edition), Springer
L. B. Bowerman, R.T. O'Connell (1993) Forecasting and Time Series, 3rd Edition, Duxbury
Materials:Lecture Notes

Planned Learning Activities and Teaching Methods

Lecture format, built around the textbook readings and R-project applications with examples chosen to illustrate theoretical concepts. Applications and examples. Questions are encouraged and discussion of material stressed. Lecture, project and presentation.

Assessment Methods

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

Turkish

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: +90-232-3018603

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 13 3 39
Preparation for final exam 1 30 30
Preparing presentations 2 40 80
Final 1 2 2
TOTAL WORKLOAD (hours) 190

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7
LO.155
LO.2555
LO.3555
LO.4544
LO.55445
LO.655
LO.755

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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