Sunspot time series prediction using parallel-structure fuzzy system

  • Authors:

  • Min-Soo Kim;Chan-Soo Chung

  • Affiliations:

  • Sejong-Lockheed Martin Aerospace Research Center, Sejong University, Seoul, Korea;Electrical Engineering Department, Soongsil University, Seoul, Korea

  • Venue:
  • FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
  • Year:
  • 2005

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Abstract

Sunspots are dark areas that grow and decay on the lowest level of the Sun that are visible from the Earth. Short-term predictions of solar activity are essential to help plan missions and to design satellites that will survive for their useful lifetimes. This paper presents a parallel-structure fuzzy system (PSFS) for prediction of sunspot number time series. The PSFS consists of a multiple number of component fuzzy systems connected in parallel. Each component fuzzy system in the PSFS predicts future data independently based on its past time series data with different embedding dimension and time delay. An embedding dimension determines the number of inputs of each component fuzzy system and a time delay decides the interval of inputs of the time series. According to the embedding dimension and the time delay, the component fuzzy system takes various input-output pairs. The PSFS determines the final predicted value as an average of all the outputs of the component fuzzy systems in order to reduce error accumulation effect.