基于ELM改进层集成架构的时间序列预测

Time series forecasting based on ELM improved layered ensemble architecture

为进一步提高时间序列预测模型的预测精度和时间效率,提出一种基于极限学习机的层集成网络结构。以极限学习机网络作为基学习器,构成两层集成网络,每层网络在构建时利用先分类,再从类中选优的思想同时考虑基学习器的准确性与多样性,其中第一层用以优化参数,第二层实现预测。对比实验结果表明,与基于多层感知器的层集成网络相比,该模型在提高预测准确度的同时将学习用时缩短了1-2个数量级。

To further improve the prediction accuracy and efficiency of the time-series prediction model,an extreme learning machine based layered ensemble network was proposed. Extreme learning machine networks were taken as base predictors and consisted of two ensemble layers. Each layer considered both accuracy and diversity of the individual networks in constructing the ensemble by taking the strategy in which the best was selected after classification. The first ensemble layer was used to find an appropriate lag,while the second one was used to employ the obtained lag for forecasting. The proposed model was tested in comparison experiments. The results reveal clearly that compared with the layered ensemble architecture based on multilayer perceptron,the proposed model not only improves the prediction accuracy,but also improves the time efficiency by 1-2 orders of magnitude.