The use of sequential recurrent neural filters in forecasting the Dst index for the strong magnetic storm of autumn 2003

Ouarbya, Lahcen; Mirikitani, Derrick Takeshi and Martin, Eamonn. 2012. The use of sequential recurrent neural filters in forecasting the Dst index for the strong magnetic storm of autumn 2003. Applied Mathematics Letters, 25(10), pp. 1361-1366. ISSN 0893-9659 [Article]

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Abstract or Description

Neural based geomagnetic forecasting literature has heavily relied upon non-sequential algorithms for estimation of model parameters. This paper proposes sequential Bayesian recurrent neural filters for online forecasting of the Dst index. Online updating of the RNN parameters allows for newly arrived observations to be included into the model. The online RNN filters are compared to two (non-sequentially trained) models on a severe double storm that has so far been difficult to forecast. It is shown that the proposed models can significantly reduce forecast errors over non-sequentially trained recurrent neural models.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1016/j.aml.2011.12.002

Keywords:

Forecasting magnetospheric disturbances, Recurrent neural networks

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
5 December 2011Accepted
17 December 2011Published Online
October 2012Published

Item ID:

30982

Date Deposited:

11 Jan 2022 11:43

Last Modified:

11 Jan 2022 11:43

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

https://research.gold.ac.uk/id/eprint/30982

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