Reconstruction of time series with missing value using 2D representation-based denoising autoencoder  

作　　者：TAO Huamin DENG Qiuqun XIAO Shanzhu 

机构地区：[1]National Key Laboratory of Science and Technology on Automatic Target Recognition,College of Electronic Science,National University of Defense Technology,Changsha 410073,China

出　　处：《Journal of Systems Engineering and Electronics》2020年第6期1087-1096,共10页系统工程与电子技术（英文版）

摘　　要：Time series analysis is a key technology for medical diagnosis,weather forecasting and financial prediction systems.However,missing data frequently occur during data recording,posing a great challenge to data mining tasks.In this study,we propose a novel time series data representation-based denoising autoencoder(DAE)for the reconstruction of missing values.Two data representation methods,namely,recurrence plot(RP)and Gramian angular field(GAF),are used to transform the raw time series to a 2D matrix for establishing the temporal correlations between different time intervals and extracting the structural patterns from the time series.Then an improved DAE is proposed to reconstruct the missing values from the 2D representation of time series.A comprehensive comparison is conducted amongst the different representations on standard datasets.Results show that the 2D representations have a lower reconstruction error than the raw time series,and the RP representation provides the best outcome.This work provides useful insights into the better reconstruction of missing values in time series analysis to considerably improve the reliability of timevarying system.

关 键 词：time series missing value 2D representation denoising autoencoder(DAE) RECONSTRUCTION 

分 类 号：TN762[电子电信—电路与系统] O211.61[理学—概率论与数理统计]