Nonlinear Time Series Prediction Performance Using Constrained Motion Particle Swarm Optimization

Abstract

Constrained Motion Particle Swarm Optimization (CMPSO) is a general framework for optimizing Support Vector Regression (SVR) free parameters for nonlinear time series regression and prediction.  CMPSO uses Particle Swarm Optimization (PSO) to determine the SVR free parameters.  However, CMPSO attempts to fuse the PSO and SVR algorithms by constraining the SVR Lagrange multipliers such that every PSO epoch yields a candidate solution that meets the SVR constraint criteria.  The fusion of these two algorithms provides a numerical efficiency advantage since an SVR Quadratic Program (QP) solver is not necessary for every particle at every epoch.  This reduces the search space of the overall optimization problem.  It has been demonstrated that CMPSO provides similar (and in some cases superior) performance to other contemporary time series prediction algorithms for nonlinear time series benchmarks such as Mackey-Glass data.  This paper details the CMPSO algorithm framework and tests its performance against other SVR time series prediction algorithms and data including the European Network on Intelligent Technologies for Smart Adaptive Systems (EUNITE) competition data and the Competition on Artificial Time Series (CATS) competition data.