Support Vector Regression for Time Series Analysis

Renato Leone ()
Additional contact information
Renato Leone: Università di Camerino

A chapter in Operations Research Proceedings 2010, 2011, pp 33-38 from Springer

Abstract: Abstract In the recent years, Support Vector Machines (SVMs) have demonstrated their capability in solving classification and regression problems. SVMs are closely related to classical multilayer perceptron Neural Networks (NN). The main advantage of SVM is that their optimal weights can be obtained by solving a quadratic programming problem with linear constraints, and, therefore, standard, very efficient algorithms can be applied. In this paper we present a 0–1 mixed integer programming formulation for the financial index tracking problem. The model is based on the use of SVM for regression and feature selection, but the standard 2–norm of the vector w is replaced by the 1–norm and binary variables are introduced to impose that only a limited number of features are utilized. Computational results on standard benchmark instances of the index tracking problems demonstrate that good quality solution can be achieved in a limited amount of CPU time.

Date: 2011
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:oprchp:978-3-642-20009-0_6

Ordering information: This item can be ordered from
http://www.springer.com/9783642200090

DOI: 10.1007/978-3-642-20009-0_6

Access Statistics for this chapter

More chapters in Operations Research Proceedings from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().