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	1. Assouad dimension and recent development
	Miao Jun Jie
	Mathematics 23 May 2016
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:In this article, we will review the definition of Assouad dimension and it background, and the fundamental properties will be summarised. Finally the Assouad dimension of self-similar sets, self-conformal sets and some self-affine set will be introduced.
	TO cite this article:Miao Jun Jie. Assouad dimension and recent development[OL].[23 May 2016] http://en.paper.edu.cn/en_releasepaper/content/4694226


	2. Time-Dependent Hurst Exponent in Traffic Time Series
	Jianhai Yue,Keqiang Dong,Pengjian Shang
	Mathematics 01 February 2010
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:In this paper, we propose a new measure of variability, called the time-dependent Hurst exponent H(t), which fully captures the degree of variability of traffic flow at each time t. In order to assess the accuracy of the technique, we calculate the exponent H(t) for artificial series with assigned Hurst exponents H. We next calculate the exponent H(t) for the traffic time series observed on the Beijing Yuquanying highway. We find a much more pronounced time-variability in the local scaling exponent of traffic series compared to the artificial ones. In addition, the results show that the traffic variability can exhibit a non-monotonic multi-fractal behavior.
	TO cite this article:Jianhai Yue,Keqiang Dong,Pengjian Shang. Time-Dependent Hurst Exponent in Traffic Time Series[OL].[ 1 February 2010] http://en.paper.edu.cn/en_releasepaper/content/39713


	3. Multifractal detrended cross-correlation analysis for two nonstationary signals
	Wei-Xing Zhou
	Mathematics 27 March 2008
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	Abstract:It is ubiquitous in natural and social sciences that two variables, recorded temporally or spatially in a complex system, are cross-correlated and possess multifractal features. We propose a new method called multifractal detrended cross-correlation analysis (MF-DXA) to investigate the multifractal behaviors in the power-law cross-correlations between two records in one or higher dimensions. The method is validated with cross-correlated 1D and 2D binomial measures and multifractal random walks. Application to two financial time series is also illustrated.
	TO cite this article:Wei-Xing Zhou. Multifractal detrended cross-correlation analysis for two nonstationary signals[OL].[27 March 2008] http://en.paper.edu.cn/en_releasepaper/content/19811

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