COURSE UNIT TITLE

: TIME SERIES ANALYSIS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 4040 TIME SERIES ANALYSIS COMPULSORY 2 2 0 7

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR ESIN FIRUZAN

Offered to

Statistics

Course Objective

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,
2   To perform the Trend Analysis and enable to interpret the output,
3   To perform the Decomposition Method and enable to interpret the output,
4   To perform the Exponential Smoothing Method and enable to interpret the output,
5   To identify Nonseasonal Box-Jenkins models using autocorrelation and partial autocorrelation function,
6   To test significance of parameter estimates of tentatively identified ARIMA (p,d,q) models,
7   To make decisions whether models are adequate, make prediction
8   To be able to compare the methodological results among Trend Analysis, Decomposition Method, Exponential Smoothing Method and ARIMA(p,d,q) models and able to make decision among them.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Week 1 What is Time Series How does the time series data distinguish
2 Week 2 What is Time Series Components What is Trend Analysis
3 Week 3 Perform the trend analysis using Linear, quadratic, log linear, exponential trend models and generate forecasting from the models
4 Week 4 Decomposition Methods-Additive Decomposition Models
5 Week 5 Decomposition Methods-Multiplicative Decomposition Models
6 Week 6 Exponential Smoothing Methods-Simple (Single) Smoothing
7 Week 7 Exponential Smoothing Methods-Double and Triple Exponential Smoothing
8 Week 8 Autocorrelation and Partial Autocorrelation Function Tentatively identification of Nonseasonal Box-Jenkins Models
9 Week 9 Autoregressive Model AR(p)
10 Week 10 Moving Average MA(q)
11 Week 11 Mixed Autoregressive Moving Average Model ARMA(p,q), ARIMA(p,d,q)
12 Week 12 Estimation-Diagnostic Checking-Forecasting
13 Week 13 Seasonal ARIMA(p,d,q)(P,D,Q)
14 Week 14 Neural Network-LSTM
15 Week 15 Presentation and evaluation of the Project

Recomended or Required Reading

Textbook(s)/References/Materials
Wei, W.W.S., 2006, Time Series Analysis, Univariate and Multivariate Methods, 2nd EdnPearson
Supplementary Book(s):
Bowerman L. B., O Connell R. T. (1993) Forecasting and Time Series, 3rd Edition, Duxbury

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.

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


Further Notes About Assessment Methods

None

Assessment Criteria

Exam, Quiz, Project and Presentation

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. 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 61

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 5 70
Tutorials 2 5 10
Preparations before/after weekly lectures 10 1 10
Preparation for midterm exam 1 20 20
Preparation for final exam 1 25 25
Project Preparation 3 6 18
Preparing presentations 1 9 9
Midterm 1 2 2
Final 1 2 2
Project Final Presentation 4 5 20
TOTAL WORKLOAD (hours) 186

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.1555445
LO.2555445
LO.3555445
LO.4555445
LO.5555445
LO.6555445
LO.7555445
LO.8555555444

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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