Paper Info
Title
Constrained Motion Particle Swarm Optimization and Support Vector Regression for Non-linear Time Series Regression and Prediction Applications
Abstract
Support Vector Regression (SVR) has been applied to many non-linear time series prediction applications [1]. There are many challenges associated with the use of SVR for non-linear time series prediction, including the selection of free parameters associated with SVR training. To optimize SVR free parameters, many different approaches have been investigated, including Particle Swarm Optimization (PSO). This paper proposes a new approach, termed Constrained Motion Particle Swarm Optimization (CMPSO), which selects SVR free parameters and solves the SVR quadratic programming (QP) problem simultaneously. To benchmark the performance of CMPSO, Mackey-Glass non-linear time series data is used for validation. Results show CMPSO performance is consistent with other time series prediction methodologies, and in some cases superior.
Year
DOI
Venue
2013
10.1109/ICMLA.2013.164
ICMLA (2)
Keywords
Field
DocType
constrained motion particle swarm,particle swarm optimization,support vector regression,free parameter,svr quadratic programming,mackey-glass non-linear time series,time series prediction methodology,cmpso performance,prediction applications,non-linear time series prediction,motion particle swarm optimization,non-linear time series regression,svr training,svr free parameter,quadratic programming,regression analysis,support vector machines,time series
Time series,Nonlinear system,Regression analysis,Computer science,Artificial intelligence,Quadratic programming,Particle swarm optimization,Mathematical optimization,Pattern recognition,Support vector machine,Multi-swarm optimization,Machine learning,Free parameter
Conference
Citations 
PageRank 
References 
1
0.41
5
Authors
2
Name
Order
Citations
PageRank
Nicholas I. Sapankevych11726.97
Ravi Sankar265655.66