Applying REC analysis to ensembles of particle filters

Particle filters (PF) are sequential Monte Carlo methods based in the representation of probability densities with mass points. Although currently most researches involving time series forecasting use the traditional methods, particle filters can be applied to any state-space model and generalize the traditional Kalman filter methods, providing better results. Furthermore, it is well-known that for classification and regression tasks ensembles achieve better performances than the algorithms that compose them. Therefore, it is expected that ensembles of time series predictors can provide even better results than particle filters. The regression error characteristic (REC) analysis is a powerful technique for visualization and comparison of regression models. The objective of this work is to advocate the use of REC curves in order to compare traditional Kalman filter methods with particle filters and analyze their use in ensembles, which can achieve a better performance. This work is an extended version of the paper presented at the 2007 International Joint Conference on Neural Networks (IJCNN) 1].