Constrained Motion Particle Swarm Optimization and Support Vector Regression for Non-linear Time Series Regression and Prediction Applications

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.