An Online Transfer Learning Framework for Time-Varying Distribution Data Prediction
Yan‐Hui Lin, Liang Chang
Abstract
Data-driven methods, especially those based on deep learning, have been successfully applied to various tasks. However, training and testing data may have different distributions in many real cases, which lead to unreliable results. Transfer learning can address this issue by learning domain-invariant features. Online transfer learning (OTL), as one type of transfer learning, deals with the situation where the data of the target domain arrive in a sequential manner. The limitations of the existing OTL methods are that they cannot deal with the online data generated by time-varying distribution because of varying operational conditions, for example, besides, they mainly focus on classification tasks. In this article, we propose a novel OTL framework for a regression task, which includes an offline stage and an online stage. In the former, a basic model is built to learn domain-invariant features. In the latter, an updating strategy is proposed to adapt the prediction model to the new data. Besides, an ensemble approach is further developed to avoid over- or underfitting and also fully utilize the knowledge from the source domain for the target domain. The effectiveness of the proposed framework is verified by a real dataset regarding the diesel hydrofining process collected from a petrochemical workshop.