Bayesian Multi-task Learning for Dynamic Time Series Prediction

Time series prediction typically consists of a data reconstruction phase where the time series is broken into overlapping windows. The size of the window could vary for different types of problems for optimal performance. Dynamic time series prediction refers to "on the fly" robust prediction given partial information where prediction can be made regardless of the window size. Multi-task learning features learning from related tasks through shared representation knowledge which has shown to be useful for dynamic time series prediction. This features uncertainty that can be addressed through synergy of Bayesian inference and multi-task learning. In this paper, we present a Bayesian approach to multi-task learning for dynamic time series prediction. The method provides uncertainty quantification given posterior distribution of weights and biases in a cascaded multi-task network architecture. The results show that the proposed method is able to provide competing prediction performance to the literature, featuring uncertainty quantification in prediction.