Earticle

초록

영어

Financial time series prediction is regarded as one of the most challengingtasks due to the inherent noise and non-stationality of the data. This paperproposed a two-stage financial time series prediction approach hybridizingsupport vector regression (SVR) with hierarchical clustering (HC). By averaging the variables within the clusters obtained from hierarchical clustering, we define super predictors and use them as the input variables of the SVRforecasting model. Although averaging is a simple technique, it plays animportant role in reducing variance. To evaluate the performance of theproposed approach, the Shanghai-Shenzhen 300 index is used as illustrativeexample. The experimental results show that the proposed approach outperforms both the SVR model with principal component analysis and the SVRmodel with genetic algorithms in average prediction error and predictionaccuracy.

목차

Abstract
 1. Introduction
 2. Methodology
  2.1. Hierarchical Clustering
  2.2. E-Support Vector Regression
 3. The Proposed System Framework
 4. Experiment Design
  4.1. Datasets and Variables
  4.2. Evaluation Strategies
 5. Experiment Results
  5.1. Determining the Level of HC
  5.2. Comparison
 6. Conclusions
 References

키워드

저자

• ZheGao [ Guanghua School of Management, Peking University, Beijing 100871, China, Peking University Founder Group, Beijing, 100871, China ] Corresponding author
• JianjunYang [ Department of Intelligence Science, Center for Information Science, Room 2314,Science Building 2, Peking University, Beijing 100871, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605