DocumentCode
2194827
Title
A Convex Combination of Models for Predicting Road Traffic
Author
Bellosta, Carlos J Gil
Author_Institution
Datanalytics, Madrid, Spain
fYear
2010
fDate
13-13 Dec. 2010
Firstpage
1354
Lastpage
1356
Abstract
This paper describes an approach to the road traffic prediction problem in Warsaw in the context of a data mining competition that is part of the IEEE ICDM 2010. A solution based on a convex combination of models mining different wells of information within the data is described. Such convex combination allows the final model compensate highly uncorrelated errors from the different underlying models and to achieve higher prediction accuracy.
Keywords
data mining; road traffic; traffic control; IEEE ICDM 2010; convex combination; data mining competition; model compensate; prediction accuracy; road traffic prediction; uncorrelated errors; underlying models; c control; data mining; forecasting; predictive modeling; road traffic;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Mining Workshops (ICDMW), 2010 IEEE International Conference on
Conference_Location
Sydney, NSW
Print_ISBN
978-1-4244-9244-2
Electronic_ISBN
978-0-7695-4257-7
Type
conf
DOI
10.1109/ICDMW.2010.23
Filename
5693450
Link To Document