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

Abstract

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 time-varying system.