Ordinal language of antipersistent binary walks

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.