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The statistical distribution of values in the signal and the autocorrelations(interpreted as the memory or persistence) between values are attributes ofa time series. The autocorrelation function values are positive in a timeseries with persistence, while they are negative in a time series with anti-persistence.The persistence of values with respect to each other can bestrong, weak, or nonexistent. A strong correlation implies a "memory" ofprevious values in the time series. The long-range persistence in time seriescould be studied using semivariograms, rescaled range, detrended fluctuationanalysis and Fourier spectral analysis, respectively. In this work,persistence analysis is to study interplanetary magnetic field (IMF) time series. We use data fromthe IMF components with a time resolution of 16 s. Time intervalscorresponding to distinct processes around 41 magnetic clouds (MCs) in the period betweenMarch 1998 and December 2003 were selected. In this exploratory study, thepurpose of this selection is to deal with the cases presenting the threeperiods: plasma sheath, MC, and post-MC. We calculated one exponent ofpersistence (e.g., α, β, Hu, Ha) over theprevious three time intervals. The persistence exponent values increasedinside cloud regions, and it was possible to select the following thresholdvalues: α(j) = 1.392,Ha(j) = 0.327, and Hu(j) = 0.875. These values areuseful as another test to evaluate the quality of the identification. If thecloud is well structured, then the persistence exponent values exceedthresholds. In 80.5% of the cases studied, these tools were able toseparate the region of the cloud from neighboring regions. The Hausdorffexponent (Ha) provides the best results

Nonlinear fluctuation analysis for a set of 41 magnetic clouds measured by the Advanced Composition Explorer (ACE) spacecraft