Multi-Task Modular Backpropagation For Dynamic Time Series Prediction

摘要

In certain types of problems, such as emerging storms or cyclones, robust prediction is needed even when partial information is available. Dynamic time series prediction refers to “on the fly” prediction given partial information. Recently, a neu-roevolution approach called co-evolutionary multi-task learning has been proposed to provide robust prediction for dynamic time series. In this paper, we adapt the method with multi-task modular backpropagation that features gradient descent and transfer learning. The method is tested on benchmark chaotic time series problems and compared with its counterparts. The results show that the method can alleviate the problems associated with timely convergence of the neuroevolution approach and provides better performance.