Dynamic correlation approach to early stopping in artificial neural network training: macroeconomic forecasting example

Abstract

Neural networks are widely used in time-series forecasting. One of the issues that arise in neural networks applications is that when a neural network is trained for too long the quality of the predictions tends to deteriorate. To overcome this problem various methods of early stopping are employed. This paper proposes a new approach to early stopping issue in neural network training. In the approach presented the validation series is chosen based on its mean dynamic correlation with forecasted series. The approach is verified by application to macroeconomic data where suitable sets of series are commonly available.