Time Series Imputation Method Combining Tensor Completion and Recurrent Neural Network
The existing imputation methods are roughly divided into statistical methods and deep learning methods.The statistical methods can only capture the linear time relationship,which makes it impossible to accurately capture the relationship of non-linear time series data.The deep learning imputation methods usually donot consider the correlation between different time series.To solve these problems,a new model jointing the tensor completion and the recurrent neural network is proposed.Firstly,the multivariate time series are modeled as a tensor,and the correlation of different time series is captured by low rank tensor completion.Secondly,a time based dynamic weight is proposed to fuse the tensor completion results with the prediction results of the recurrent neural network to avoid the accumulation of prediction error caused by continuous missing.The proposed method is evaluated on several real time series datasets,and the results show that the proposed model outperforms the existing models in term of imputation accuracy,which is helpful for improving classification and regression accuracy.
tensor completiontime series imputationrecurrent neural network
万方数据
维普
