Abstract
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.
Original language | English |
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Pages (from-to) | 5080-5090 |
Number of pages | 11 |
Journal | Physica A: Statistical Mechanics and its Applications |
Volume | 387 |
Issue number | 21 |
DOIs | |
State | Published - 1 Sep 2008 |
Bibliographical note
Funding Information:We thank Diego Rybski for very helpful discussions. This work has been supported by the Deutsche Forschungsgemeinschaft (grant KA 1676/3) and the European Union (STREP project DAPHNet, grant 018474-2). RB acknowledges financial support from the President scholarship of Bar-Ilan University.
Funding
We thank Diego Rybski for very helpful discussions. This work has been supported by the Deutsche Forschungsgemeinschaft (grant KA 1676/3) and the European Union (STREP project DAPHNet, grant 018474-2). RB acknowledges financial support from the President scholarship of Bar-Ilan University.
Funders | Funder number |
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President scholarship of Bar-Ilan University | |
European Commission | 018474-2 |
Deutsche Forschungsgemeinschaft | KA 1676/3 |
Keywords
- Crossovers
- Detrended fluctuation analysis
- Long-range correlations
- Non-stationarities
- Time series analysis