Improvement of Chaotic Short-term Forecasting on Fuzzy Reasoning and Tuning on Genetic Algorithm

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  • ファジィ推論によるカオス短期予測の改善と遺伝的アルゴリズムによるチューニング(<特集>ソフトサイエンス)
  • ファジィ推論によるカオス短期予測の改善と遺伝的アルゴリズムによるチューニング
  • ファジィ スイロン ニ ヨル カオス タンキ ヨソク ノ カイゼン ト イデンテキ アルゴリズム ニ ヨル チューニング

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Abstract

Widely chaos model is employed to forecast a value in short term future using such time-series data that its regularity is hardly found. The chaotic short-term forecasting method is based on Takens Embedding Theorem which enables us to reconstruct an attractor in multi-dimensional space using such data that seem to be random. Even if data has no random nature but deterministic and geometrical nature, it is also hard to forecast their future values based on the chaos method if the data cannot be reconstruct in sufficiently low dimensional space. This paper proposes the method to embed another reference data related closely to the original focal data. The method can enable us to abstract the chaotic portion out of the focal data and increase the forecasting precision. In the chaotic forecasting method based on Euclidean distance, the fuzzy reasoning is employed in order to deal with vague information included in the focal data. These two methods are evaluated by simulation examination on the comparison with a conventional method. The simulation result shows its effectiveness by applying the method to forecasting the future value of Nikkei Mean Value of Tokyo Stock Market. It should be noted that fuzzy membership functions used in premises of rules in fuzzy reasoning are automatically and optimally tuned by genetic algorithm and that the forecasting model is rebuild easily.

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