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Characterization of time series through information quantifiers

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  • Liu, Zhengli
  • Shang, Pengjian
  • Wang, Yuanyuan

Abstract

The paper proposes to apply the K-Fisher (KF) index and the complexity-entropy curves based on k-entropy into studying the time series. The KF index is a good extension of the traditional Shannon-Fisher (SF) index. In the complexity-entropy curves, the results show that the normalized k-entropy and its corresponding complexity has the same extreme value k=0.6, interestingly, the KF index is the smallest when k=0.6, which suggests that the time series with k=0.6 might be the most stable and verifies that the KF index is an effective tool to measure the stability of sequence. Furthermore, we conclude that this new complexity-entropy curves and the KF index could clearly distinguish financial stock indices and find that the stability of US stock indices are higher than the Australian and Chinese stock indices.

Suggested Citation

  • Liu, Zhengli & Shang, Pengjian & Wang, Yuanyuan, 2020. "Characterization of time series through information quantifiers," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    • Handle: RePEc:eee:chsofr:v:132:y:2020:i:c:s0960077919305223
    DOI: 10.1016/j.chaos.2019.109565
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    References listed on IDEAS

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