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.

中文翻译：

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使用基于2D表示的去噪自动编码器重建具有缺失值的时间序列

时间序列分析是医学诊断，天气预报和财务预测系统的关键技术。但是，数据记录期间经常会丢失数据，这对数据挖掘任务构成了巨大挑战。在这项研究中，我们提出了一种新颖的基于时间序列数据表示的去噪自动编码器（DAE），用于重建缺失值。使用两种数据表示方法，即递归图（RP）和格拉姆角场（GAF），将原始时间序列转换为2D矩阵，以建立不同时间间隔之间的时间相关性并从时间序列中提取结构模式。然后提出一种改进的DAE，以从时间序列的2D表示中重建缺失值。在标准数据集的不同表示形式之间进行了全面的比较。结果表明，二维表示的重构误差比原始时间序列低，而RP表示提供了最佳结果。这项工作为更好地重建时间序列分析中的缺失值提供了有用的见解，从而大大提高了时变系统的可靠性。