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.
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