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Effective instability quantification for multivariate complex time series using reverse Shannon-Fisher index

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  • Shang, Binbin
  • Shang, Pengjian

Abstract

In this paper, we propose a reverse form of Shannon-Fisher (SF) index. The new method is based on the original SF index, which is capable of quantifying the instability of time series. The core of this method mainly includes two points. Firstly, considering that the visibility graph (VG) is only suitable for univariate time series, we replace it with the vector visibility graph (VVG) applicable for multivariate time series. Secondly, in order to provide a new perspective to explore the information contained in time series, we change the original distribution applied in the SF index to the negation of this original distribution. Compared with the original SF index, our new method can not only obtain the information contained in simulated time series from a complementary point of view, but also improve the limitation of data length in the original SF index. In the process of experimenting with the stock data, it can be realized that our method is able to identify those special years of different regions, which suggests that it is provided with the possibility of becoming a new effective method for instability quantification of multivariate time series.

Suggested Citation

  • Shang, Binbin & Shang, Pengjian, 2022. "Effective instability quantification for multivariate complex time series using reverse Shannon-Fisher index," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    • Handle: RePEc:eee:chsofr:v:160:y:2022:i:c:s0960077922005057
    DOI: 10.1016/j.chaos.2022.112295
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    References listed on IDEAS

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