Comparison of detrending methods for fluctuation analysis
Comparison of detrending methods for fluctuation analysis
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DOI：
10.1016/j.physa.2008.04.023
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发表时间：
2008-09-01
影响因子：
3.3
通讯作者：
Havlin, Shlomo
中科院分区：
文献类型：
--
作者：
Bashan, Amir;Bartsch, Ronny;Havlin, Shlomo
我们研究了最近提出的几种用于检测数据序列中远程相关性的方法，这些方法基于与已建立的去趋势波动分析（DFA）类似的思想。特别是，我们详细比较了常规DFA和最近提出的两种方法：中心移动平均（CMA）方法和修正的去趋势波动分析（MDFA）。我们发现CMA在趋势较弱的长数据中的表现与DFA相同，在趋势较弱的短数据中的表现略优于DFA。当比较标准DFA和MDFA时，我们观察到DFA在我们研究的几乎所有示例中都稍好一些。我们还讨论了几种趋势如何影响不同类型的DFA。对于数据中的弱趋势，新方法在这些方面可与DFA相比较。但是，如果数据趋势的函数形式不是先验已知的，则DFA仍然是选择的方法。只有对DFA结果进行比较，使用不同的趋势多项式，才能充分认识到趋势。建议用与独立方法的比较来证明远距离相关性。(C) 2008 Elsevier B.V.版权所有
We examine several recently suggested methods for the detection of long-range correlations in data series based on similar ideas as the well-established Detrended Fluctuation Analysis (DFA). In particular, we present a detailed comparison between the regular DFA and two recently suggested methods: the Centered Moving Average (CMA) Method and a Modified Detrended Fluctuation Analysis (MDFA). We find that CMA performs the same as DFA in long data with weak trends and is slightly superior to DFA in short data with weak trends. When comparing standard DFA to MDFA we observe that DFA performs slightly better in almost all examples we studied. We also discuss how several types of trends affect different types of DFA. For weak trends in the data, the new methods are comparable with DFA in these respects. However, if the functional form of the trend in data is not a-priori known, DFA remains the method of choice. Only a comparison of DFA results, using different detrending polynomials, yields full recognition of the trends. A comparison with independent methods is recommended for proving long-range correlations. (C) 2008 Elsevier B.V. All rights reserved.