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Time series prediction typically consists of a data reconstruction phasewhere the time series is broken into overlapping windows known as the timespan.The size of the timespan can be seen as a way of determining the extent of pastinformation required for an effective prediction. In certain applications suchas the prediction of wind-intensity of storms and cyclones, prediction modelsneed to be dynamic in accommodating different values of the timespan. Theseapplications require robust prediction as soon as the event takes place. Weidentify a new category of problem called dynamic time series prediction thatrequires a model to give prediction when presented with varying lengths of thetimespan. In this paper, we propose a co-evolutionary multi-task learningmethod that provides a synergy between multi-task learning and co-evolutionaryalgorithms to address dynamic time series prediction. The method featureseffective use of building blocks of knowledge inspired by dynamic programmingand multi-task learning. It enables neural networks to retain modularity duringtraining for making a decision in situations even when certain inputs aremissing. The effectiveness of the method is demonstrated using one-step-aheadchaotic time series and tropical cyclone wind-intensity prediction.

Co-evolutionary multi-task learning for dynamic time series prediction