%0 Journal Article
%A CHEN Zipeng
%A LI Sen
%A MA Qianli
%A QIAN Bin
%A XIAO Yong
%A YE Chunzhuang
%A ZHENG Kaihong
%T Time series imputation model based on long-short term memory network with residual connection
%D 2021
%R 10.11772/j.issn.1001-9081.2020060928
%J Journal of Computer Applications
%P 243-248
%V 41
%N 1
%X Traditional time series imputation methods typically assume that time series data is derived from a linear dynamic system. However, the real-world time series show more non-linear characteristics. Therefore, a time series imputation model based on Long Short-Term Memory (LSTM) network with residual connection, called RSI-LSTM (ReSidual Imputation Long-Short Term Memory), was proposed to capture the non-linear dynamic characteristics of time series effectively and mine the potential relation between missing data and recent non-missing data. Specifically, the LSTM network was used to model the underlying non-linear dynamic characteristics of time series, meanwhile, the residual connection was introduced to mine the connection between the historical values and the missing value to improve the imputation capability of the model. Firstly, RSI-LSTM was applied to impute the missing data of the univariate daily power supply dataset, and then on the power load dataset of the 9th Electrical Engineering Mathematical Modeling Competition problem A, the meteorological factors were introduced as the multivariate input of RSI-LSTM to improve the imputation performance of the model on missing value in the time series. Furthermore, two general multivariate time series datasets were used to verify the missing value imputation ability of the model. Experimental results show that compared with LSTM, RSI-LSTM can obtain better imputation performance, and has the Mean Square Error (MSE) 10% lower than LSTM generally on both univariate and multivariate datasets.
%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2020060928