Abstract
Particle filters (PF) are sequential Monte Carlo methods based in the representation of probability densities with mass points. They can be applied to any state-space model and generalize the traditional Kalman filter methods, providing better results. However, currently most researches involving time series forecasting use the traditional methods. The 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.
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