IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/615152.html
   My bibliography  Save this article

A Trend-Based Segmentation Method and the Support Vector Regression for Financial Time Series Forecasting

Author

Listed:
  • Jheng-Long Wu
  • Pei-Chann Chang

Abstract

This paper presents a novel trend-based segmentation method (TBSM) and the support vector regression (SVR) for financial time series forecasting. The model is named as TBSM-SVR. Over the last decade, SVR has been a popular forecasting model for nonlinear time series problem. The general segmentation method, that is, the piecewise linear representation (PLR), has been applied to locate a set of trading points within a financial time series data. However, owing to the dynamics in stock trading, PLR cannot reflect the trend changes within a specific time period. Therefore, a trend based segmentation method is developed in this research to overcome this issue. The model is tested using various stocks from America stock market with different trend tendencies. The experimental results show that the proposed model can generate more profits than other models. The model is very practical for real-world application, and it can be implemented in a real-time environment.

Suggested Citation

  • Jheng-Long Wu & Pei-Chann Chang, 2012. "A Trend-Based Segmentation Method and the Support Vector Regression for Financial Time Series Forecasting," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-20, May.
    • Handle: RePEc:hin:jnlmpe:615152
    DOI: 10.1155/2012/615152
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2012/615152.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2012/615152.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2012/615152?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:615152. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.