Comparing the performance of FA, DFA and DMA using different synthetic long-range correlated time series
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 528785494 | ||
| 005 | 20181217030312.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 180924e20121112xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.3929/ethz-b-000059162 |2 doi |
| 024 | 7 | 0 | |a 10.1038/srep00835 |2 doi |
| 035 | |a (ETHRESEARCH)oai:www.research-collecti.ethz.ch:20.500.11850/59162 | ||
| 245 | 0 | 0 | |a Comparing the performance of FA, DFA and DMA using different synthetic long-range correlated time series |h [Elektronische Daten] |c [Ying-Hui Shao, Gao-Feng Gu, Zhi-Qiang Jiang, Wei-Xing Zhou, Didier Sornette] |
| 246 | 0 | |a Sci Rep | |
| 506 | |a Open access |2 ethresearch | ||
| 520 | 3 | |a Notwithstanding the significant efforts to develop estimators of long-range correlations (LRC) and to compare their performance, no clear consensus exists on what is the best method and under which conditions. In addition, synthetic tests suggest that the performance of LRC estimators varies when using different generators of LRC time series. Here, we compare the performances of four estimators [Fluctuation Analysis (FA), Detrended Fluctuation Analysis (DFA), Backward Detrending Moving Average (BDMA), and Centred Detrending Moving Average (CDMA)]. We use three different generators [Fractional Gaussian Noises, and two ways of generating Fractional Brownian Motions]. We find that CDMA has the best performance and DFA is only slightly worse in some situations, while FA performs the worst. In addition, CDMA and DFA are less sensitive to the scaling range than FA. Hence, CDMA and DFA remain "The Methods of Choice” in determining the Hurst index of time series. | |
| 540 | |a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported |u http://creativecommons.org/licenses/by-nc-nd/3.0 |2 ethresearch | ||
| 690 | 7 | |a Information theory and computation |2 ethresearch | |
| 690 | 7 | |a Statistical physics, thermodynamics and nonlinear dynamics |2 ethresearch | |
| 690 | 7 | |a Statistics |2 ethresearch | |
| 690 | 7 | |a Software |2 ethresearch | |
| 700 | 1 | |a Shao |D Ying-Hui |e joint author | |
| 700 | 1 | |a Gu |D Gao-Feng |e joint author | |
| 700 | 1 | |a Jiang |D Zhi-Qiang |e joint author | |
| 700 | 1 | |a Zhou |D Wei-Xing |e joint author | |
| 700 | 1 | |a Sornette |D Didier |e joint author | |
| 773 | 0 | |t Scientific Reports |d London : Nature Publishing Group |g 2, p. 835 |x 2045-2322 | |
| 856 | 4 | 0 | |u http://hdl.handle.net/20.500.11850/59162 |q text/html |z WWW-Backlink auf das Repository (Open access) |
| 908 | |D 1 |a Journal Article |2 ethresearch | ||
| 950 | |B ETHRESEARCH |P 856 |E 40 |u http://hdl.handle.net/20.500.11850/59162 |q text/html |z WWW-Backlink auf das Repository (Open access) | ||
| 950 | |B ETHRESEARCH |P 700 |E 1- |a Shao |D Ying-Hui |e joint author | ||
| 950 | |B ETHRESEARCH |P 700 |E 1- |a Gu |D Gao-Feng |e joint author | ||
| 950 | |B ETHRESEARCH |P 700 |E 1- |a Jiang |D Zhi-Qiang |e joint author | ||
| 950 | |B ETHRESEARCH |P 700 |E 1- |a Zhou |D Wei-Xing |e joint author | ||
| 950 | |B ETHRESEARCH |P 700 |E 1- |a Sornette |D Didier |e joint author | ||
| 950 | |B ETHRESEARCH |P 773 |E 0- |t Scientific Reports |d London : Nature Publishing Group |g 2, p. 835 |x 2045-2322 | ||
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 949 | |B ETHRESEARCH |F ETHRESEARCH |b ETHRESEARCH |j Journal Article |c Open access | ||