Hybrid neural system for time series prediction

In this paper, we present an incremental modular system of times series prediction. This system is hybrid and is based on three methods, an incremental self organizing map (e-SOM) for dynamically learning the past of the times series, an ascendant hierarchical clustering (AHC) for optimizing the num...

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Veröffentlicht in:ITI 2006 : proceedings of the 28th International Conference on Information Technology Interfaces : June 19-22, 2006, Cavtat/Dubrovnik, Croatia S. 349 - 354
1. Verfasser: Benabdeslem, K.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 2006
Schlagworte:
Data visualization
Displays
Finance
Iterative algorithms
Multilayer perceptrons
Neurons
Optimization methods
Organizing
Statistical analysis
ISBN:9789537138059, 9537138054
ISSN:1330-1012
Online-Zugang:Volltext
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Zusammenfassung:In this paper, we present an incremental modular system of times series prediction. This system is hybrid and is based on three methods, an incremental self organizing map (e-SOM) for dynamically learning the past of the times series, an ascendant hierarchical clustering (AHC) for optimizing the number of classes forming the map and a set of local multilayer perceptrons (MLPs) for predicting the evolution of data in the future. The number of MLPs depends on the number of classes formed by AHC. Our approach called (ILM) is compared with several other methods like a global approach only based on MLP and modular one using SOM and MLP. We demonstrate that ILM method is rather more efficient than the other previous methods
ISBN:9789537138059
9537138054
ISSN:1330-1012
DOI:10.1109/ITI.2006.1708505