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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Axford, W.I., Hines, C.O.: A unifying theory of high-latitude geophysical phenomena and geomagnetic storms. Can. J. Phys. 39, 1433–1464 (1961)
de Freitas, J.F.G., Niranjan, M., Gee, A.H.: Dynamic Learning with the EM Algorithm for Neural Networks. J. Vlsi. Signal. Proc. 26, 119–131 (2000)
Dungey, J.W.: Interplanetary magnetic field and the auroral zones. Phys. Rev. Lett. 26, 47–48 (2000)
Freeman, J., Natal, A., Reiff, P., Denig, W., Gussenhoven-Shea, S., Heinemann, M., Rich, F., Hairston, M.: The use of neural networks to predict magnetospheric parameters for input to a magnetospheric forecast model. In: Proceedings of Artificial Intelligence Applications in Solar-Terrestrial Physics Workshop, pp. 167–181 (1993)
Gonzales, W.D., Joselyn, J.A., Kamide, Y., Kroehl, H.W., Rostoker, G., Tsurutani, B.T., Vasyliunas, V.M.: What is a geomagnetic storm? J. Geophys. Res. 99, 5771–5792 (1994)
Ghahramani, Z., Hinton, G.E.: Parameter estimation for linear dynamical systems. Technical Report, University of Toronto CRG-TR-96-2 (1996)
Lundstedt, H.: Neural Networks and prediction of solar-terrestrial effects. Planet. Space Sci. 40, 457–464 (1992)
Lundstedt, H., Wintoft, P.: Prediction of geomagnetic storms from solar wind data with the use of a neural network. Ann Geophys. 12, 19–24 (1994)
Lundstedt, H., Gleisner, H., Wintoft, P.: Operational forecasts of the geomagnetic Dst index. Geophys. Res. Lett. 29(24), 34–1–34–4 (2002)
Pallocchia, G., Amata, E., Consolini, G., Marcucci, M.F., Bertello, I.: Geomagnetic Dst index forecast based on IMF data only. Ann. Geophys. 24, 989–999 (2006)
Puskorius, G.V., Feldkamp, L.A.: Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks. IEEE T Neural Networks 5(2), 279–297 (1994)
Rauch, H.E., Tung, F., Striebel, C.T.: Maximum Likelihood Estimates of Linear Dynamic Models. AIAA Journal 3(8), 1445–1450 (1965)
Schottky, B., Saad, D.: Statistical mechanics of EKF learning in neural networks. J. Phys. A 32(9), 1605–1621 (1999)
Shumway, R.H., Stoffer, D.S.: An Approach To Time Series Smoothing and Forecasting Using the EM Algorithm. J. Time Ser. Anal. 4, 253–264 (1982)
Wei, H.L., Bilings, S.A., Balikhin, M.A.: Prediction of the Dst index using multiresolution wavelet models. J. Geophys. Res. 109(A7), A07212 (2004)
W.H.L., Bilings, S.A., Balikhin, M.A.: Forecasting the Geomagnetic Activity of teh Dst index using Multiscale Radial Basis Function Networks. In: Advances in Space Research, vol. 40, pp. 1863–1870 (2007)
Wu, J.G., Lundstedt, H.: Geomagnetic storm predictions from solar wind data with the use of dynamic neural networks. J. Geophys. Res. 102(A7), 14–255 (1997)
Williams, R.J., Zipser, D.: A learning algorithm for continuously running fully connected recurrent neural networks. Neural Computation 1, 270–280 (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mirikitani, D.T., Ouarbya, L. (2009). Modeling D st with Recurrent EM Neural Networks. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_100
Download citation
DOI: https://doi.org/10.1007/978-3-642-04274-4_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04273-7
Online ISBN: 978-3-642-04274-4
eBook Packages: Computer ScienceComputer Science (R0)