SRLI: Handling Irregular Time Series with a Novel Self-supervised Model Based on Contrastive Learning

The advancement of sensor technology has made it possible to use more sensors to monitor industrial systems, resulting in a large amount of irregular, unlabeled time-series data. Consequently, a large volume of irregular and unlabeled time series data is produced. Learning appropriate representations for those series is a very important but challenging task. This paper presents a self-supervised representation learning model SRLI (Self-supervised Representation Learning for Irregularities). We use T-LSTM to construct the irregularity encoder block. Based on this, we design three data augmentation methods. First, the raw time-series data are transformed into different yet correlated views. Second, we propose a contrasting module to learn robust representations. Lastly, to further learn discriminative representations, we reconstruct the series and try to get the imputation values of the unobserved positions. Rather than in a two-stage manner, our framework can generate the instance-level representation for ISMTS directly. Experiments show that this model has good performance on multiple data sets.