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Inspired by the interesting idea of randomization, some powerful but time-consuming decomposition-ensemble learning paradigms can be extended into extremely efficient and fast variants by using randomized algorithms as individual forecasting tools. In the proposed methodology, Three major steps, (1) data decomposition via ensemble empirical mode decomposition, (2) individual prediction via a randomized...
The forecasting of electricity price remains the subject of increasingly intense research attention as the market structure becomes more complicated with the deregulation waves and the increasing level of price fluctuations observed. The heterogeneous data structure revealed in the recent empirical studies serves as the important stylized fact to be explored and analyzed in the heterogeneous market...
To improve the forecasting accuracy of crude oil price with deeper understanding of the market microstructure, this paper proposes a wavelet decomposed ensemble model. The proposed model follows the Heterogeneous Market Hypothesis that assumes the unstationarity and dynamic changing nature of the underlying market structure and introduces the wavelet analysis to analyze the dynamic underlying Data...
Due to the distinct seasonal characteristics of hydropower, this study tries to propose a seasonal decomposition (SD) based least squares support vector regression (LSSVR) ensemble learning model for Chinese hydropower consumption forecasting. In the formulation of ensemble learning model, the original hydropower consumption series are first decomposed into trend cycle, seasonal factor and irregular...
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