Ordinal language of antipersistent binary walks
arXiv:2503.11784 · doi:10.1016/j.physleta.2024.130017
Abstract
This paper explores the effectiveness of using ordinal pattern probabilities to evaluate antipersistency in the sign decomposition of long-range anti-correlated Gaussian fluctuations. It is numerically shown that ordinal patterns are able to effectively measure both persistent and antipersistent dynamics by analyzing the sign decomposition derived from fractional Gaussian noise. These findings are crucial given that traditional methods such as Detrended Fluctuation Analysis are unsuccessful in detecting anti-correlations in such sequences. The numerical results are supported by physiological and environmental data, illustrating its applicability in real-world situations.
arXiv comment12 pages, 4 figures
References in corpus (9)
- Detecting Long-range Correlations with Detrended Fluctuation Analysis
- The statistical properties of the volatility of price fluctuations
- Robustness and resilience of complex networks
- Magnitude and Sign Correlations in Heartbeat Fluctuations
- Characterization of Sleep Stages by Correlations of Heartbeat Increments
- Power law correlations in the Southern Oscillation Index fluctuations characterizing El Nino
- Volatility of Linear and Nonlinear Time Series
- Permutation Jensen-Shannon distance: A versatile and fast symbolic tool for complex time series analysis
- Noisy-chaotic time series and the forbidden/missing patterns paradigm