Semi-supervised contrastive regression for pharmaceutical processes

Artificial intelligence methods of time series are starting to play an increasing role in the pharmaceutical field, and in recent years, there have been significant advances in self-supervised representation learning for time series data. However, there are relatively few semi-supervised learning methods for time series, and there is almost no research on semi-supervised representation learning applicable to time series regression tasks. To address this gap, we propose a novel semi-supervised contrastive regression framework (SCRF), which combines two classical frameworks of representation learning. This framework is well-suited for regression problems of time series data from pharmaceutical processes and has been validated on a dataset collected during erythromycin production processes. Our experiments show that SCRF gets better performances than self-supervised and supervised methods, and it is more robust to missing labels, missing data, and random noise. The effectiveness of our novel contrastive learning framework and segmented augmentation methods is demonstrated through experiments. •Adaptive segmented augmentation method.•Supervised contrastive loss for regression task.•Novel semi-supervised contrastive learning framework.•Representation learning for pharmaceutical time series.