Applying REC Analysis to Ensembles of Sigma-Point Kalman Filters

  • Aloísio Carlos de Pina
  • Gerson Zaverucha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


The Sigma-Point Kalman Filters (SPKF) is a family of filters that achieve very good performance when applied to time series. Currently most researches involving time series forecasting use the Sigma-Point Kalman Filters, however they do not use an ensemble of them, which could achieve a better performance. 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 the SPKF and ensembles of them and select the best model to be used.


Receiver Operating Characteristic Curve Kalman Filter Extend Kalman Filter Base Learner Unscented Kalman Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Aloísio Carlos de Pina
    • 1
  • Gerson Zaverucha
    • 1
  1. 1.Department of Systems Engineering and Computer ScienceFederal University of Rio de Janeiro, COPPE/PESCRio de JaneiroBrazil

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