DocumentCode :
2652728
Title :
Self-Organized Hierarchical Methods for Time Series Forecasting
Author :
Abinader, F.M. ; de Queiroz, A.C.S. ; Honda, D.W.
Author_Institution :
Univ. Fed. do Rio Grande do Norte, Natal, Brazil
fYear :
2011
fDate :
7-9 Nov. 2011
Firstpage :
1057
Lastpage :
1062
Abstract :
Time series forecasting with the use of Artificial Neural Networks (ANN), in special with self-organized maps (SOM), has been explored in the literature with good results. One good strategy for improving computational cost and specialization of SOMs in general is constructing it via hierarchical structures. This work presents four different heuristics for constructing hierarchical SOMs for time series prediction, evaluating their computational cost and forecast precision and providing insight on future enhancements.
Keywords :
mathematics computing; self-organising feature maps; time series; artificial neural networks; forecast precision; self organized hierarchical methods; self organized maps; time series forecasting; Forecasting; Heuristic algorithms; Prototypes; Time series analysis; Training; Vectors; Vegetation; Hierarchical SOM; Self-Organized Maps; Time Series Forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on
Conference_Location :
Boca Raton, FL
ISSN :
1082-3409
Print_ISBN :
978-1-4577-2068-0
Electronic_ISBN :
1082-3409
Type :
conf
DOI :
10.1109/ICTAI.2011.180
Filename :
6103471
Link To Document :
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