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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Continuous Dynamical Combination of Short and Long-Term Forecasts for Nonstationary Time Series
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Continuous Dynamical Combination of Short and Long-Term Forecasts for Nonstationary Time Series

机译：非平稳时间序列的短期和长期预测的连续动力组合

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摘要

This brief generalizes the forecasting method that has been awarded first-place winner in the International Competition of Time Series Forecasting (ICTSF 2012). It is based on a short-term forecasting approach of multilayer perceptrons (MLP) ensembles, combined dynamically with a long-term forecasting. The main feature of this general approach is the original concept of continuous dynamical combination of forecasts, in which the weights of the forecasting combination are a function of forecast horizon. Experiments in ICTSFs and NN5s nonstationary time series show that this new combination method improves the performance in multistep forecasting of MLP ensembles when compared to the MLP ensembles alone.
机译：本摘要概括了在国际时间序列预测竞赛（ICTSF 2012）中获得第一名的预测方法。它基于多层感知器（MLP）集合的短期预测方法，并与长期预测动态结合。这种通用方法的主要特征是连续动态组合预测的原始概念，其中预测组合的权重是预测范围的函数。 ICTSF和NN5非平稳时间序列的实验表明，与单独的MLP集成相比，这种新的组合方法提高了MLP集成的多步预测性能。

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