Detrended fluctuation analysis (DFA) is a scaling method that allows the detection of long memory in a time series. Until now no asymptotic distribution has been found for this statistic. The bootstrap technique allows the simulation of the probability distribution of any statistic. In this paper th
Comparison of detrending methods for fluctuation analysis
โ Scribed by Amir Bashan; Ronny Bartsch; Jan W. Kantelhardt; Shlomo Havlin
- Publisher
- Elsevier Science
- Year
- 2008
- Tongue
- English
- Weight
- 995 KB
- Volume
- 387
- Category
- Article
- ISSN
- 0378-4371
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โฆ Synopsis
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 longrange correlations.
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