COURSE UNIT TITLE

: BUSINESS FORECASTING

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EMT 4034 BUSINESS FORECASTING ELECTIVE 3 0 0 5

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ÖZLEM KIREN GÜRLER

Offered to

Econometrics
Econometrics (Evening)

Course Objective

The main objective of the course is to provide a comprehensive introduction to forecasting methods and to present enough information about each method for students to be able to use them sensibly in the field of business forecasting applications.

Learning Outcomes of the Course Unit

1   To be able to describe the importance of business forecasting.
2   To be able to employ judgemental forecasting techniques.
3   To be able to model the components of a time series.
4   To be able to use state space models.
5   To be able to perform various forecasting techniques with the help of R programming.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 1 Forecasting, the Why and How.
2 2 Basic Tools for Forecasting.
3 3 Judgmental forecasts
4 4 The Delphi method, Scenario forecasting.
5 5 New product forecasting, Judgmental adjustments.
6 6 Time series components, X-12-ARIMA decomposition
7 7 STL decomposition, Forecasting with decomposition.
8 8 A taxonomy of exponential smoothing methods
9 9 Innovations state space models for exponential smoothing
10 10 Stationarity and differencing, Non-seasonal ARIMA models, Seasonal ARIMA models
11 11 Stationarity and differencing, Non-seasonal ARIMA models, Seasonal ARIMA models
12 12 Advanced forecasting methods: Neural network models, Hierarchical or grouped time series
13 13 Combined forecasting methods, Using R
14 14 Combined forecasting methods, Using R

Recomended or Required Reading

Ord, K., & Fildes, R. (2013). Principles of business forecasting. Cengage Learning.
Armstrong, J. S. (Ed.). (2001). Principles of forecasting: a handbook for researchers and practitioners (Vol. 30). Springer Science & Business Media.
Hyndman, R. J., & Athanasopoulos, G. (2014). Forecasting: principles and practice. OTexts.

Planned Learning Activities and Teaching Methods

This course will be presented using class lectures, class discussions, overhead projections, and demonstrations.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 STT TERM WORK (SEMESTER)
2 MTE MIDTERM EXAM
3 MTEG MIDTERM GRADE STT * 0.50 +MTE * 0.50
4 FIN FINAL EXAM
5 FCG FINAL COURSE GRADE MTEG * 0.40 + FIN * 0.60
6 RST RESIT
7 FCGR FINAL COURSE GRADE (RESIT) MTEG * 0.40 + RST * 0.60


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

serkan.aras@deu.edu.tr

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 2 28
Preparation for final exam 1 10 10
Preparation for quiz etc. 1 10 10
Preparing assignments 1 25 25
Preparing presentations 1 6 6
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 125

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.111
LO.2111
LO.31111
LO.4111
LO.511111

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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