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We propose a novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting. Our method is based on the principles of “data-characteristic-based modeling” and “decomposition and ensemble”. The model improves on existing decomposition ensemble learning techniques (with “decomposition and ensemble”) by using “data-characteristic-based modeling” to forecast the...
Most of the existing models for oil price forecasting only use the data in the forecasted time series itself. This study proposes a transfer learning based analog complexing model (TLAC). It first transfers some related time series in source domain to assist in modeling the target time series by transfer learning technique, and then constructs the forecasting model by analog complexing method. Finally,...
In this study, a generalized Intelligent-agent-based fuzzy group forecasting model is proposed for oil price prediction. In the proposed model, some single Intelligent-agent-based predictors with much disagreement are first created for crude oil price prediction. Then these single prediction results produced by these single intelligent predictors are fuzzified into some fuzzy prediction representations...
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