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Deriving domain knowledge from unstructured information: A forecasting framework based on judgmental adjustment approach

International Conference on Man Machine Systems (ICoMMS 2012) organized by School of Mechatronic Engineering, co-organized by The Institute of Engineer, Malaysia (IEM) and Society of Engineering Education Malaysia, 27th - 28th February 2012 at Bayview Beach Resort, Penang, Malaysia.

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Bibliographic Details
Main Authors: Wendy Japutra Jap, Then, Patrick Hang Hui, Dr.
Other Authors: wjap@swinburne.edu.my
Format: Working Paper
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2012
Subjects:
Time series
Forecasting
Judgmental adjustment
News article
Forecasting support system
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/20712
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id my.unimap-20712
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spelling my.unimap-207122012-08-15T08:36:26Z Deriving domain knowledge from unstructured information: A forecasting framework based on judgmental adjustment approach Wendy Japutra Jap Then, Patrick Hang Hui, Dr. wjap@swinburne.edu.my pthen@swinburne.edu.my Time series Forecasting Judgmental adjustment News article Forecasting support system International Conference on Man Machine Systems (ICoMMS 2012) organized by School of Mechatronic Engineering, co-organized by The Institute of Engineer, Malaysia (IEM) and Society of Engineering Education Malaysia, 27th - 28th February 2012 at Bayview Beach Resort, Penang, Malaysia. The integration of quantitative and judgmental forecasting methods have been increasingly applied to give better performance to forecast. Judgmental adjustment is one instance of integrating both methods and it has been gaining recognition among forecasting practitioners because of its quick and convenient way to perform forecast. However, many criticize this approach because of its disadvantages, i.e. bias and inconsistency which are associated to the human. We are proposing a forecasting framework that aids the process of judgmental adjustment by providing supportive information to reduce the effect of bias and inconsistency. The proposed framework comprises five different modules, i.e. time series graphical display, quantitative forecast, news-based supportive information, user comment and similaritybased pattern search. 2012-08-15T08:36:26Z 2012-08-15T08:36:26Z 2012-02-27 Working Paper http://hdl.handle.net/123456789/20712 en Proceedings of the International Conference on Man-Machine Systems (ICoMMS 2012) Universiti Malaysia Perlis (UniMAP) School of Mechatronic Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Time series
Forecasting
Judgmental adjustment
News article
Forecasting support system
spellingShingle Time series
Forecasting
Judgmental adjustment
News article
Forecasting support system
Wendy Japutra Jap
Then, Patrick Hang Hui, Dr.
Deriving domain knowledge from unstructured information: A forecasting framework based on judgmental adjustment approach
description International Conference on Man Machine Systems (ICoMMS 2012) organized by School of Mechatronic Engineering, co-organized by The Institute of Engineer, Malaysia (IEM) and Society of Engineering Education Malaysia, 27th - 28th February 2012 at Bayview Beach Resort, Penang, Malaysia.
author2 wjap@swinburne.edu.my
author_facet wjap@swinburne.edu.my
Wendy Japutra Jap
Then, Patrick Hang Hui, Dr.
format Working Paper
author Wendy Japutra Jap
Then, Patrick Hang Hui, Dr.
author_sort Wendy Japutra Jap
title Deriving domain knowledge from unstructured information: A forecasting framework based on judgmental adjustment approach
title_short Deriving domain knowledge from unstructured information: A forecasting framework based on judgmental adjustment approach
title_full Deriving domain knowledge from unstructured information: A forecasting framework based on judgmental adjustment approach
title_fullStr Deriving domain knowledge from unstructured information: A forecasting framework based on judgmental adjustment approach
title_full_unstemmed Deriving domain knowledge from unstructured information: A forecasting framework based on judgmental adjustment approach
title_sort deriving domain knowledge from unstructured information: a forecasting framework based on judgmental adjustment approach
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/20712
_version_ 1643793158941704192
score 13.214268

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