Nonstationarity and data preprocessing for neural network predictions of an economic time series

The presence of stochastic or deterministic trends in economic time series can be a major obstacle for producing satisfactory predictions with neural networks. In this paper, we demonstrate the effects of nonstationarity on neural network predictions using the time series of the mortgage loans purchased in the Netherlands. We present different preprocessing techniques for removing nonstationarity, and evaluate their properties by producing multi-step predictions using a linear stochastic forecasting model and a neural network. The results indicate that detecting nonstationarity and selecting an appropriate preprocessing technique is highly beneficial for improving the prediction quality.