An e–E-insensitive support vector regression machine

Amir Safari ()

Computational Statistics, 2014, vol. 29, issue 6, 1447-1468

Abstract: According to the Statistical Learning Theory, the support vectors represent the most informative data points and compress the information contained in training set. However, a basic problem in the standard support vector machine is that when the data is noisy, there exists no guaranteed scheme in support vector machines’ formulation to dissuade the machine from learning noise. Therefore, the noise which is typically presents in financial time series data may be taken into account as support vectors. In turn, noisy support vectors are modeled into the estimated function. As such, the inclusion of noise in support vectors may lead to an over-fitting and in turn to a poor generalization. The standard support vector regression (SVR) is reformulated in this article in such a way that the large errors which correspond to noise are restricted by a new parameter $$E$$ E . The simulation and real world experiments indicate that the novel SVR machine meaningfully performs better than the standard SVR in terms of accuracy and precision especially where the data is noisy, but in expense of a longer computation time. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Financial time series; Loss function; Noise process; Support vector regression machine (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1007/s00180-014-0500-7 (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:spr:compst:v:29:y:2014:i:6:p:1447-1468

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-014-0500-7

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().