An intelligent clustering forecasting system based on change-point detection and artificial neural networks: Application to financial economics

Kyong Joo Oh, Ingoo Han

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

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.

Original languageEnglish
Number of pages1
JournalProceedings of the Hawaii International Conference on System Sciences
DOIs
Publication statusPublished - 2001 Jan 1

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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