Time series prediction via neural network inversion

In this work, we propose neural network inversion of a backward predictor as a technique for multi-step prediction of dynamic time series. It may be di cult to train a large network to capture the correlation that exists in some dynamic time series represented by small data sets. The new approach combines an estimate obtained from a forward predictor with an estimate obtained by inverting a backward predictor to more e ciently capture the correlation and to achieve more accurate predictions. Inversion allows us to make causal use of prediction backward in time. Also a new regularization method is developed to make neural network inversion less ill-posed. Experimental results on two benchmark time series demonstrate the new approach's signi cant improvement over standard forward prediction, given comparable complexity.