Time sequence complexity measurement method based on image micro-structure frequency analysis

Abstract

The invention provides a time sequence complexity measurement method based on the image micro-structure frequency analysis. The method comprises the steps of constructing a signal recursive matrix; drawing a gray image according to the recursive matrix at the recursive state of the i moment and at the recursive state of the j moment; filtering the gray image according to Gaussian kernel functions of different scales so as to obtain different Gaussian gray images and form a Gaussian pyramid; subjecting the Gaussian kernel functions of different scales and the gray images to the convolution operation to obtain the scale space of the images; in the above scale space, preliminarily determining the positions and the scales of feature points; conducting the least-squares fitting based on the secondary expansion equation of the Taylor function of a scale-space function, and removing unstable feature points by utilizing the extreme values of a fitting surface; clustering remaining feature points; continuously changing the values of an influence degree and a validity degree through calculating; subjecting an obtained clustering result to information measurement; calculating the complexities of different signals by using an approximate entropy and a permutation entropy; and subjecting the results of microstructure recursive entropies to comparative analysis. The above method provides a beneficial reference for the design of novel entropy methods.

Description

technical field [0001] The invention relates to a method for measuring and calculating the complexity of time series. In particular, it relates to a time series complexity measurement method based on image microstructure frequency analysis. Background technique [0002] Complexity science, which emerged in the 1980s, is a new stage in the development of system science and one of the frontiers of contemporary scientific development. The development of complexity science has not only triggered changes in the natural sciences, but has also increasingly penetrated into the fields of philosophy, humanities and social sciences. With the popularity of big data, data-driven methods have become an important means of studying complex systems, and time series analysis has become a powerful tool for exploring and interpreting the inner operating mechanism of complex systems. Related achievements have been widely used in physics, economics, Meteorology, linguistics and information scie...

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Problems solved by technology

The study found that the existing methods are mainly limited to design entropy methods in the time domain and frequency domain, so the complexity analysis is somewhat one-sided

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