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Long-term prediction of solar and geomagnetic activity daily time series using singular spectrum analysis and fuzzy descriptor models

Earth, Planets and Space volume 61, pages 1089–1101 (2009)Cite this article

Abstract

Of the various conditions that affect space weather, Sun-driven phenomena are the most dominant. Cyclic solar activity has a significant effect on the Earth, its climate, satellites, and space missions. In recent years, space weather hazards have become a major area of investigation, especially due to the advent of satellite technology. As such, the design of reliable alerting and warning systems is of utmost importance, and international collaboration is needed to develop accurate short-term and long-term prediction methodologies. Several methods have been proposed and implemented for the prediction of solar and geomagnetic activity indices, but problems in predicting the exact time and magnitude of such catastrophic events still remain. There are, however, descriptor systems that describe a wider class of systems, including physical models and non-dynamic constraints. It is well known that the descriptor system is much tighter than the state-space expression for representing real independent parametric perturbations. In addition, the fuzzy descriptor models as a generalization of the locally linear neurofuzzy models are general forms that can be trained by constructive intuitive learning algorithms. Here, we propose a combined model based on fuzzy descriptor models and singular spectrum analysis (SSA) (FD/SSA) to forecast a number of geomagnetic activity indices in a manner that optimizes a fuzzy descriptor model for each of the principal components obtained from singular spectrum analysis and recombines the predicted values so as to transform the geomagnetic activity time series into natural chaotic phenomena. The method has been applied to predict two solar and geomagnetic activity indices: geomagnetic aa and solar wind speed (SWS) of the solar wind index. The results demonstrate the higher power of the proposed method—compared to other methods—for predicting solar activity.

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Authors and Affiliations

  1. Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University College of Engineering, University of Tehran, Tehran, Iran

    Masoud Mirmomeni & Caro Lucas

  2. Computer Engineering Department, Sharif University of Technology, Tehran, Iran

    E. Kamaliha

  3. Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran

    Masoud Shafiee

  4. School of Cognitive Sciences, Institute for studies in theoretical Physics and Mathematics, Tehran, Iran

    Caro Lucas

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  1. Masoud Mirmomeni
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  2. E. Kamaliha
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  3. Masoud Shafiee
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  4. Caro Lucas
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Correspondence to Masoud Mirmomeni.

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Mirmomeni, M., Kamaliha, E., Shafiee, M. et al. Long-term prediction of solar and geomagnetic activity daily time series using singular spectrum analysis and fuzzy descriptor models. Earth Planet Sp 61, 1089–1101 (2009). https://doi.org/10.1186/BF03352959

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