Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.