This article suggests a new clustering forecasting system to integrate change-point detection and artificial neural networks. The basic concept of proposed model is to obtain intervals divided by change point, 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, is to detect successive change points in dataset and to forecast 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) model-based approach. The next stage is to forecast the final output with BPN. Through the application to the financial economics, we compare the proposed models with a neural network model alone and, in addition, determine which of three change point detection methods can perform better. This article is then to examine the predictability of the integrated neural network model based on change-point detection.
|Number of pages||1|
|Journal||Proceedings of the Hawaii International Conference on System Sciences|
|Publication status||Published - 2001 Jan 1|
All Science Journal Classification (ASJC) codes
- Computer Science(all)