CLeaR: An Adaptive Continual Learning Framework for Regression Tasks

Catastrophic forgetting means that a trained neural network model gradually forgets the previously learned tasks when retrained on new tasks. Overcoming the forgetting problem is a major problem in machine learning. Numerous continual learning algorithms are very successful in incremental classification tasks, where new labels appear frequently. However, there is currently no research that addresses the catastrophic forgetting problem in regression tasks as far as we know. This problem has emerged as one of the primary constraints in some applications, such as renewable energy forecasts. This article clarifies the problem-related definitions and proposes a new methodological framework that can forecast regression task targets and update itself by continual learning. The framework consists of forecasting neural networks and buffers, which store newly collected data from a data stream in an application. Changes in the probability distribution of the data stream will be identified by the framework and learned sequentially. The framework is called CLeaR (Continual Learning for Regression Tasks), where components can be flexibly customized for a specific application scenario. We design two sets of experiments to evaluate the CLeaR framework concerning fitting error (training), prediction error (test), and forgetting ratio. The first one is based on an artificial time series to explore how hyperparameters affect the CLeaR framework. The second one is designed with data collected from European wind farms to evaluate the performance of the CLeaR framework in a real-world application. The experimental results demonstrate that the CLeaR framework can efficiently accumulate knowledge in the data stream and improve the prediction accuracy. The article concludes with further research issues arising from requirements to extend the framework.