title: Beyond Benford's Law: Distinguishing Noise from Chaos.
abstract: Determinism and randomness are two inherent aspects of all physical processes. Time series from chaotic systems share several features identical with those generated from stochastic processes, which makes them almost undistinguishable. In this paper, a new method based on Benford's law is designed in order to distinguish noise from chaos by only information from the first digit of considered series. By applying this method to discrete data, we confirm that chaotic data indeed can be distinguished from noise data, quantitatively and clearly.
authors.affiliation: Laboratory for Climate and Ocean-Atmosphere Studies, Dept. of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China.
authors.forename: Qinglei
authors.initials: Q
authors.lastname: Li
authors.affiliation: Laboratory for Climate and Ocean-Atmosphere Studies, Dept. of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China.
authors.forename: Zuntao
authors.initials: Z
authors.lastname: Fu
authors.affiliation: Laboratory for Climate and Ocean-Atmosphere Studies, Dept. of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China; Department of Geography, Climatology, Climate Dynamics, and Climate Change, Justus-Liebig University Giessen, Giessen, Germany.
authors.forename: Naiming
authors.initials: N
authors.lastname: Yuan
mesh_terms: D003627:Data Interpretation, Statistical; D019451:Information Management / Q000379:methods*; D008962:Models, Theoretical; D009622:Noise