  Time series prediction typically consists of a data reconstruction phase
where 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 past
information required for an effective prediction. In certain applications such
as the prediction of wind-intensity of storms and cyclones, prediction models
need to be dynamic in accommodating different values of the timespan. These
applications require robust prediction as soon as the event takes place. We
identify a new category of problem called dynamic time series prediction that
requires a model to give prediction when presented with varying lengths of the
timespan. In this paper, we propose a co-evolutionary multi-task learning
method that provides a synergy between multi-task learning and co-evolutionary
algorithms to address dynamic time series prediction. The method features
effective use of building blocks of knowledge inspired by dynamic programming
and multi-task learning. It enables neural networks to retain modularity during
training for making a decision in situations even when certain inputs are
missing. The effectiveness of the method is demonstrated using one-step-ahead
chaotic time series and tropical cyclone wind-intensity prediction.
