  A regularized artificial neural network (RANN) is proposed for
interval-valued data prediction. The ANN model is selected due to its powerful
capability in fitting linear and nonlinear functions. To meet mathematical
coherence requirement for an interval (i.e., the predicted lower bounds should
not cross over their upper bounds), a soft non-crossing regularizer is
introduced to the interval-valued ANN model. We conduct extensive experiments
based on both simulation datasets and real-life datasets, and compare the
proposed RANN method with multiple traditional models, including the linear
constrained center and range method (CCRM), the least absolute shrinkage and
selection operator-based interval-valued regression method (Lasso-IR), the
nonlinear interval kernel regression (IKR), the interval multi-layer perceptron
(iMLP) and the multi-output support vector regression (MSVR). Experimental
results show that the proposed RANN model is an effective tool for
interval-valued prediction tasks with high prediction accuracy.
