Data compression and learning in time sequences analysis

DOI10.1016/S0167-2789(03)00047-2zbMath1094.68567arXivcond-mat/0207321OpenAlexW2048283878MaRDI QIDQ1870470

Dario Benedetto, Angelo Vulpiani, Emanuele Caglioti, Vittorio Loreto, Andrea Puglisi

Publication date: 13 May 2003

Published in: Physica D (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/cond-mat/0207321

zbMATH Keywords

relative entropyscaling functioncross-over length

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Algorithmic information theory (Kolmogorov complexity, etc.) (68Q30) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Coding and information theory (compaction, compression, models of communication, encoding schemes, etc.) (aspects in computer science) (68P30)

Related Items (7)

The construction of nonlinear discrete-time observers with local Lipschitz conditions ⋮ Relative entropy via non-sequential recursive pair substitution ⋮ Transition matrix analysis of earthquake magnitude sequences ⋮ An example of mathematical authorship attribution ⋮ Expanding the algorithmic information theory frame for applications to Earth observation ⋮ Artificial sequences and complexity measures ⋮ Non-sequential recursive pair substitution: some rigorous results

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