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Multi-step prediction of Dst index using singular spectrum analysis and locally linear neurofuzzy modeling

Abstract

Even one-step prediction of natural time series without delay especially in main phase of storm is difficult for many complicated time series such as Dst index. In this study, with a new method based on singular spectrum analysis, we extract the main components of the time series, model each component with a locally linear neurofuzzy network, and utilize the trained networks for multi-step ahead prediction of a validation set of data, and finally combine the predicted patterns for construction of general prediction. Our methods are compared with several previous studies for Dst index prediction. Several solar geomagnetic extreme events are predicted well with our state-of-the-art method; such as extreme events in 14 March 1989 that led to power black-out in Quebec, as well as other extreme storms.

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Correspondence to Javad Sharifi.

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Sharifi, J., Araabi, B.N. & Lucas, C. Multi-step prediction of Dst index using singular spectrum analysis and locally linear neurofuzzy modeling. Earth Planet Sp 58, 331–341 (2006). https://doi.org/10.1186/BF03351929

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Key words

  • Singular spectral analysis
  • locally linear neurofuzzy model
  • Dst prediction