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Suggests a new clustering forecasting system to integrate change-point detection and artificial neural networks. The basic concept of the proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to involve them in the forecasting model. The proposed models consist of two stages. The first stage (the clustering neural network modeling stage) detects successive change points in a data set and forecasts the change-point group with backpropagation neural networks (BPN). In this stage, three change-point detection methods are applied and compared: (1) the parametric method, (2) the nonparametric approach, and (3) the model-based approach. The next stage is to forecast the final output with a BPN. Through an application to financial economics, we compare the proposed models with a neural network model alone and, in addition, determine which of the three change-point detection methods can perform better. This article then examines the predictability of the integrated neural network model based on change-point detection.