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時間序列分形特征的判別

Discriminating Fractals In Time Series

  • 摘要: 研究了判斷時間序列是否具有分形特征的幾個參數:龐加萊映射、李雅普諾夫指數、關聯維數、功率譜及赫斯特指數,分析了它們各自的優缺點,認為:在已知動力學系統時,使用龐加萊映射和李雅普諾夫指數就能準確地判斷該時間序列是否分形;在不知道動力學系統時,使用功率譜及赫斯特指數更好些,最后給出了分形在時間序列分析中適用的場合。

     

    Abstract: Fractal theory is a new method to apply in time series analysis, but how to discriminate fractal time series from non-fractal time series is ambiguous. Several Parame-ters, such as Poincare map, Lyapunov exponent, correlation dimension, power spectrum density and Hunt exponent, are used to recognize if the time series are fractals. The relia-bilities of the parameters used above arc compared. If the dynamic system is known, it's fit to use the Poincare map and Lyapunov exponent; while if the dynamic system is unknown, it, s fit to use the power spectrum density and Hurst exponent. At last, the range of fractals applying in time series analysis is sketched.

     

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