Predicting Chaotic Time Series Using Support Vector Machines Optimized by Genetic Algorithm

The Support Vector Machine Theory is a hotspot in the field of machine learning in recent years. In this article a chaotic time series prediction method using Support Vector Machines and phase construction theory is introduced, and the predicting performances of several common kernel functions are compared, taking stock price time series as samples. Experiments show that Gaussian kernel obviously performs better than other kernels. Support Vector Machines with Gaussian kernel are optimized by Genetic Algorithm, and performs much better than SVMs whose parameters are decided by experience, and also better than traditional prediction methods.