2009/11/12 by Fermı́n Moscoso del Prado Martı́n, Martín, Fermín Moscoso del Prado
Computer Science · Economics, Econometrics and Finance · Physics and Astronomy · #Chaotic Dynamics (nlin.CD) #Complex Network Analysis Techniques #Complex Systems and Time Series Analysis #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Methodology (stat.ME) #Statistical Mechanics (cond-mat.stat-mech) #Statistics and Probability (physics.data-an) #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.0911.2381
openalex publication_date 2009/11/12 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/31
Current methods for determining whether a time series exhibits fractal structure (FS) rely on subjective assessments on estimators of the Hurst exponent (H). Here, I introduce the Bayesian Assessment of Scaling, an analytical framework for drawing objective and accurate inferences on the FS of time series. The technique exploits the scaling property of the diffusion associated to a time series. The resulting criterion is simple to compute and represents an accurate characterization of the evidence supporting different hypotheses on the scaling regime of a time series. Additionally, a closed-form Maximum Likelihood estimator of H is derived from the criterion, and this estimator outperforms the best available estimators.