Some evidence on forecasting time-series with support vector machines

J V Hansen (), James McDonald and R D Nelson
Additional contact information
J V Hansen: Marriott School of Management, Brigham Young University
R D Nelson: Marriott School of Management, Brigham Young University

Journal of the Operational Research Society, 2006, vol. 57, issue 9, 1053-1063

Abstract: Abstract The importance of predicting future values of a time-series transcends a range of disciplines. Economic and business time-series are typically characterized by trend, cycle, seasonal, and random components. Powerful methods have been developed to capture these components by specifying and estimating statistical models. These methods include exponential smoothing, autoregressive integrated moving average (ARIMA), and partially adaptive estimated ARIMA models. New research in pattern recognition through machine learning offers innovative methodologies that can improve forecasting performance. This paper presents a study of the comparative results of time-series analysis on nine problem domains, each of which exhibits differing time-series characteristics. Comparative analyses use ARIMA selection employing an intelligent agent, ARIMA estimation through partially adaptive methods, and support vector machines. The results find that support vector machines weakly dominate the other methods and achieve the best results in eight of nine different data sets.

Keywords: time-series; forecasting; support vector machines (search for similar items in EconPapers)
Date: 2006
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1057/palgrave.jors.2602073 Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jorsoc:v:57:y:2006:i:9:d:10.1057_palgrave.jors.2602073

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/41274

DOI: 10.1057/palgrave.jors.2602073

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald and Jonathan Crook

More articles in Journal of the Operational Research Society from Palgrave Macmillan, The OR Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().