Financial time series prediction using a support vector regression network

Boyang Li*, Jinglu Hu, Kotaro Hirasawa

*この研究の対応する著者

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

This paper presents a novel support vector regression (SVR) network for financial time series prediction. The SVR network consists of two layers of SVR: transformation layer and prediction layer. The SVRs in the transformation layer forms a modular network; but distinguished with conventional modular networks, the partition of the SVR modular network is based on the output domain that has much smaller dimension. Then the transformed outputs from the transformation layer are used as the inputs for the SVR in prediction layer. The whole SVR network gives an online prediction of financial time series. Simulation results on the prediction of currency exchange rate between US dollar and Japanese Yen show the feasibility and the effectiveness of the proposed method.

本文言語English
ホスト出版物のタイトル2008 International Joint Conference on Neural Networks, IJCNN 2008
ページ621-627
ページ数7
DOI
出版ステータスPublished - 2008 11 24
イベント2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
継続期間: 2008 6 12008 6 8

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks

Conference

Conference2008 International Joint Conference on Neural Networks, IJCNN 2008
国/地域China
CityHong Kong
Period08/6/108/6/8

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

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