Permutation entropy is an effective index which can be used to describe the dynamic complexity of a time series, and it can effectively enlarge the small changes of a sequence. In this paper, the moving cut data-permutation entropy, a new method detecting abrupt change is raised by combining the permutation entropy method with the moving cut data technology. Different moving window scales are selected to analyze the mutational detection of linear and nonlinear time series via the new method respectively. The effect of peak noise and white Gaussian noise on this new method in nonlinear time series constructed by Lorenz equation and random sequence was studied. The results show that the moving cut data-permutation entropy method has strong anti-noise ability, which is able to precisely identify the mutational point of both the linear and nonlinear time series, and almost independent the scale of window and the length of sequence.
A New Method Detecting Abrupt Change Base on Moving Cut Data-Permutation Entropy, American Journal of Applied Mathematics.
Vol. 6, No. 2,
2018, pp. 62-70.
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