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

TL;DR

This paper introduces a co-evolutionary multi-task learning approach designed for dynamic time series prediction, enabling models to adapt to varying timespans and missing data in real-time applications like cyclone wind prediction.

Contribution

It presents a novel combination of multi-task learning and co-evolutionary algorithms to improve robustness and adaptability in dynamic time series prediction tasks.

Findings

Effective in chaotic time series prediction

Robust in cyclone wind-intensity forecasting

Maintains modularity with missing inputs

Abstract

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.