OPELM and OPKNN in Long-Term Prediction of Time Series using Projected Input Data

D. Sovili, A. Sorjamma, Q. Yu, Y. Miche and E. Séverin
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E. Séverin: LEM - Lille - Economie et Management - Université de Lille, Sciences et Technologies - CNRS - Centre National de la Recherche Scientifique

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Abstract: Long-term time series prediction is a difficult task. This is due to accumulation of errors and inherent uncertainties of a long-term prediction, which leads to deteriorated estimates of the future instances. In order to make accurate predictions, this paper presents a methodology that uses input processing before building the model. Input processing is a necessary step due to the curse of dimensionality, where the aim is to reduce the number of input variables or features. In the paper, we consider the combination of the delta test and the genetic algorithm to obtain two aspects of reduction: scaling and projection. After input processing, two fast models are used to make the predictions: optimally pruned extreme learning machine and optimally pruned k-nearest neighbors. Both models have fast training times, which makes them suitable choice for direct strategy for long-term prediction. The methodology is tested on three different data sets: two time series competition data sets and one financial data set.

Date: 2010
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Citations: View citations in EconPapers (1)

Published in Neurocomputing, 2010, 73 (10-12), pp.1976-1986. ⟨10.1016/j.neucom.2009.11.033⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-00801914

DOI: 10.1016/j.neucom.2009.11.033

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