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Modeling Dst with Recurrent EM Neural Networks

  • Derrick Takeshi Mirikitani
  • Lahcen Ouarbya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5768)

Abstract

Recurrent Neural Networks have been used extensively for space weather forecasts of geomagnetospheric disturbances. One of the major drawbacks for reliable forecasts have been the use of training algorithms that are unable to account for model uncertainty and noise in data. We propose a probabilistic training algorithm based on the Expectation Maximization framework for parameterization of the model which makes use of a forward filtering and backward smoothing Expectation step, and a Maximization step in which the model uncertainty and measurement noise estimates are computed. Through numerical experimentation it is shown that the proposed model allows for reliable forecasts and also outperforms other neural time series models trained with the Extended Kalman Filter, and gradient descent learning.

Keywords

Solar Wind Root Mean Square Error Interplanetary Magnetic Field Geomagnetic Storm Extended 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 2009

Authors and Affiliations

  • Derrick Takeshi Mirikitani
    • 1
  • Lahcen Ouarbya
    • 1
  1. 1.Department of Computer Science, Goldsmiths CollegeUniversity of London, New Cross, LondonLondonUK

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