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Ensemble of grouped two-stage attention-based recurrent neural networks for multivariate time series prediction
Ensemble of grouped two-stage attention-based recurrent neural networks for multivariate time series prediction
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机译:基于分组两阶段注意的多变量时间序列预测递归神经网络集成
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摘要
A method for multivariate time series prediction is created. Each time series consisting of a set of multiple drive time series and a target time series is broken down into a raw component, a mold component and a trend component (320). For each disassembled component, a control time series relevant to it isselected from the set and hidden features of the selected drive time series are obtained by applying (330) the set to an attention-based input encoder of an ensemble of grouped two-stage attention-based recurrent neural networks (EC-DARNNS). The hidden features are displayed in a hidden space using an attention-based temporal decoEC-DARNNS groups automatically (330). Each clustered two-stage attention-based RNN in the ensemble is firmly assigned to a respective disassembled component and is applied to it. A particular value of one or more future time steps for the target series is predicted by the EC-DARNNS on the basis of the respective forecast outputs for each disassembled component (330).
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