基于门控权重单元的多变量时间序列预测

Multivariate Time Series Prediction Based on Gating Weight Unit

多变量时间序列各变量间依赖性较强,数据变化趋势不明显,预测难度高.传统研究采用带门控机制的循环神经网络及变体进行预测,但序列间存在相互依赖关系,突变数据段建模预测不精确.基于信息熵,本文提出一种新的改进门控权重单元,利用信息熵技术量化数据序列的变化程度,动态调整权重矩阵刻画数据的变化趋势.基于4个公开数据集分别进行实验,实验结果表明新模型比传统循环神经网络模型具有更好的预测性能.

There is strong dependence among the variables of multivariate time series,which makes the data trend unobvious and the prediction difficult.Traditionally,recurrent neural network with gating mechanisms and its variants are used for prediction.But the interdependence between sequences makes the prediction result of mutation data not accurate.Based on information entropy,a new modified gating weight unit is presented.The change degree of data is quantified by using information entropy to dynamically adjust the weight matrix and describe the trend of data.The experiment is conducted with four public data sets.The experimental results show that the proposed model has better prediction performance than the traditional recurrent neural network.