Detecting Long-range Correlations with Detrended Fluctuation Analysis
Jan W. Kantelhardt, Eva Koscielny-Bunde, Henio H. A. Rego, Shlomo Havlin, Armin Bunde
Abstract
We examine the Detrended Fluctuation Analysis (DFA), which is a well-established method for the detection of long-range correlations in time series. We show that deviations from scaling that appear at small time scales become stronger in higher orders of DFA, and suggest a modified DFA method to remove them. The improvement is necessary especially for short records that are affected by non-stationarities. Furthermore, we describe how crossovers in the correlation behavior can be detected reliably and determined quantitatively and show how several types of trends in the data affect the different orders of DFA.
Create a lesson
Related papers
Robust Golden Ratio universality as a consequence of a circulant symmetry
Luka Paljk, Johannes Schmidt, Vladislav Popkov
Replica Transition at Measurement-Altered Ising Criticality
Kabir Khanna, Sara Murciano, Romain Vasseur
Scalene Yang-Baxter triples realize all nearest-neighbour Hamiltonians with global additive symmetries
Pramod Padmanabhan, Somnath Maity, Akash Sinha et al.
Graph-theoretic framework for semi-Markov networks: transient and stationary occupation probabilities, hitting times and weak ergodicity breaking
Julian Voits
Spectral optimization of spherical lattices and applications to classical and quantum interacting spin systems
Jonas Völler, Grigorios Makris, Fabian Hassler et al.
Entropic-ordered superfluid
Isabella Zane, Andrew Lucas