Interval-valued data prediction via regularized artificial neural network

Abstract The prediction of interval-valued data is a challenging task as the predicted lower bounds of intervals should not cross over the corresponding upper bounds. In this paper, a regularized artificial neural network (RANN) is proposed to address this difficult problem. It provides a flexible trade-off between prediction accuracy and interval crossing. Compared to existing hard-constrained methods, the RANN has the advantage that it does not necessarily reduce the prediction accuracy while preventing interval crossing. Extensive experiments are conducted based on both simulation and real-life datasets, with comparison to multiple traditional models, including the linear constrained center and range method, the least absolute shrinkage and selection operator-based interval-valued regression, the nonlinear interval kernel regression, the interval multi-layer perceptron and the multi-output support vector regression. Experimental results show that the proposed RANN model is an effective tool for interval-valued data prediction tasks with high prediction accuracy.