A review of symbolic analysis of experimental data

By C. Daw, C. Finney, E. Tracy. 2003

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Dynamical Hierarchical Tree in Currency Markets (2007, unknown): “The theoretical setup of Symbolic Time Series Analysis is based on [18], [11] and [12].”
TSclust: An R Package for Time Series Clustering (2014, background): “…many diﬀerent symbolic approximations of time series in the literature (see, e.g., the review of symbolic analysis of experimental data provided by Daw et al. 2003), the Symbolic Aggregate ApproXimation (SAX) representation has been implemented in TSclust because allows both dimensionality…”
Dynamics of Bayesian Updating with Dependent Data and Misspecified Models (2009, background): “Despite their simplicity, these models arise naturally when studying the time series of chaotic dynamical systems [3, 15, 57, 16], as well as problems in statistical mechanics [50] and crystallography [62].”
Complex network approaches to nonlinear time series analysis (2019, background): “Notably, the latter approach combines the classical recurrence idea and basic concepts of symbolic dynamics [137] (see Section 5.”
Forbidden patterns, permutation entropy and stock market inefficiency (2009, methodology): “We can mention, among others, procedures based on Fourier analysis [26], symbolic analysis [27], amplitude statistics [28] and wavelet transform [29].”