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Research on the chaotic time series decision method

YE Xiao-zhou

Open publisher page 5 citations

Abstract

Before extracting the chaotic characteristic of the time series,it is needed to consider whether this time series have the chaos.If it is assumed that the experimental data is chaotic without examination in advance and the reconstruction phase space theory is used directly to extract the time series characteristics to build the model and the forecast,the result will be incredible.The chaotic system usually can be identified by the existence of chaotic attractors.Around this characteristics,this paper discussed the chaotic time series decision method.

About this research paper

What this paper is about

Before extracting the chaotic characteristic of the time series,it is needed to consider whether this time series have the chaos.If it is assumed that the experimental data is chaotic without examination in advance and the reconstruction phase space theory is used directly to extract the time series characteristics to build the model and the forecast,the result will be incredible.The chaotic system usually can be identified by the existence of chaotic attractors.Around this characteristics,this paper discussed the chaotic time series decision method.

Why it matters

OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Main findings

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Available abstract

Before extracting the chaotic characteristic of the time series,it is needed to consider whether this time series have the chaos.If it is assumed that the experimental data is chaotic without examination in advance and the reconstruction phase space theory is used directly to extract the time series characteristics to build the model and the forecast,the result will be incredible.The chaotic system usually can be identified by the existence of chaotic attractors.Around this characteristics,this paper discussed the chaotic time series decision method.

Key concepts: Chaotic, Attractor, Series (stratigraphy), Computer science, Time series, Phase space, Chaos theory, CHAOS (operating system)

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