基于残差连接长短期记忆网络的时间序列修复模型

Time series imputation model based on long-short term memory network with residual connection

传统的时间序列缺失修复方法通常假设数据由线性动态系统产生,然而时间序列更多地表现为非线性。为此,提出了基于残差连接长短期记忆(LSTM)网络的时间序列修复模型,称为RSI-LSTM,用来有效捕获时间序列的非线性动态特性,并且挖掘缺失数据和最近的非缺失数据之间的潜在关联。具体来说,就是采用LSTM网络对时间序列的非线性动态特性进行建模,同时引入残差连接来挖掘历史值与缺失值的联系,从而提升模型的修复能力。首先使用RSI-LSTM对单变量日供电量数据集的缺失数据进行修复,然后在第九届电工数学建模竞赛A题的电力负荷数据集上,引入气象因素作为RSI-LSTM的多变量输入,以提升模型对时间序列缺失值的修复效果。此外,使用了两个通用的多变量时间序列数据集以验证模型的缺失修复能力。实验结果表明,在单变量和多变量数据集上,RSI-LSTM的缺失值修复效果均优于LSTM,得到的均方误差(MSE)总体下降了10%。

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,RSILSTM can obtain better imputation performance,and has the Mean Square Error(MSE)10%lower than LSTM generally on both univariate and multivariate datasets.