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Applying REC analysis to ensembles of particle filters

  • IJCNN 2007
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Abstract

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.

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Acknowledgments

The sigma-point Kalman filters and sigma-point particle filters were obtained from the ReBEL Toolkit, under Academic License from OGI School of Science and Engineering, Rudolph van der Merwe and Eric A. Wan. The implementation of model trees was obtained from the WEKA system [46]. The authors were partially supported by the Brazilian Research Agency CNPq.

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Authors and Affiliations

  1. Department of Systems Engineering and Computer Science, COPPE, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil

    Aloísio Carlos de Pina & Gerson Zaverucha

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  1. Aloísio Carlos de Pina
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  2. Gerson Zaverucha
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Correspondence to Gerson Zaverucha.

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de Pina, A.C., Zaverucha, G. Applying REC analysis to ensembles of particle filters. Neural Comput & Applic 18, 25–35 (2009). https://doi.org/10.1007/s00521-008-0199-x

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