Piecewise nonlinear model for financial time series forecasting with artificial neural networks

Abstract

This study proposes a piecewise nonlinear model based on the segmentation of financial time series. The basic concept of proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in the forecasting model. The proposed model consists of two stages. The first stage detects successive change points in time series dataset and forecasts change-point groups with backpropagation neural networks (BPNs). In this stage, the following three change-point detection methods are applied and compared: the parametric method, the nonparametric approach, and the model-based approach. The next stage forecasts the final output with BPN using the groups. This study applies the proposed model to interest rate forecasting and examines three different models based on various change point detection methods. The experimental result shows that the proposed models outperforms conventional neural network model.