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Erschienen in:
2016 | OriginalPaper | Buchkapitel
WELM: Extreme Learning Machine with Wavelet Dynamic Co-Movement Analysis in High-Dimensional Time Series
verfasst von : Heng-Guo Zhang, Rui Nian, Yan Song, Yang Liu, Xuefei Liu, Amaury Lendasse
Erschienen in: Proceedings of ELM-2015 Volume 2
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Abstract
In this paper, we propose a fast and efficient learning approach called WELM based on Extreme Learning Machine and 3-D Wavelet Dynamic Co-Movement Analysis to enhance the speed and precision of big data prediction. 3-D Wavelet Dynamic Co-Movement Analysis is firstly employed to transform optimization problems from an original higher-dimensional space to a new lower-dimensional space while preserving the optimum of the original function, and then ELM is utilized to train and forecast the whole process. WELM model is used in the volatility of time series prediction. The forecasts obtained by WELM has been compared with ELM, PCA-ELM, ICA-ELM, KPCA-ELM, SVM and GARCH type models in terms of closeness to the realized volatility. The computational results demonstrate that the WELM provides better time series forecasts and it shows the excellent performance in the accuracy and efficiency.