Maintainer: Julio E. Sandubete <jsandube@ucm.es>
Imports: xts, zoo, outliers, entropy, nnet, pracma, sandwich,
Description: Provides several algorithms for the purpose of detecting chaotic signals inside univariate time series. We focus on methods derived from chaos theory which estimate the complexity of a dataset through exploring the structure of the attractor. We have taken into account the Lyapunov exponents as an ergodic measure. We have implemented the Jacobian method by a fit through neural networks in order to estimate both the largest and the spectrum of Lyapunov exponents. We have considered the full sample and three different methods of subsampling by blocks (non-overlapping, equally spaced and bootstrap) to estimate them. In addition, it is possible to make inference about them and know if the estimated Lyapunov exponents values are or not statistically significant. This library can be used with time series whose time-lapse is fixed or variable. That is, it considers time series whose observations are sampled at fixed or variable time intervals. For a review see David Ruelle and Floris Takens (1971) <doi:10.1007/BF01646553>, Ramazan Gencay and W. Davis Dechert (1992) <doi:10.1016/0167-2789(92)90210-E>, Jean-Pierre Eckmann and David Ruelle (1995) <doi:10.1103/RevModPhys.57.617>, Mototsugu Shintani and Oliver Linton (2004) <doi:10.1016/S0304-4076(03)00205-7>, Jeremy P. Huke and David S. Broomhead (2007) <doi:10.1088/0951-7715/20/9/011>.
Packaged: 2019-04-13 12:40:12 UTC; julioemilio

Imports: xts, zoo, outliers, nnet, pracma, sandwich, NeuralNetTools
Packaged: 2019-05-29 06:48:14 UTC; julioemilio

Imports: xts, zoo, outliers, nnet, pracma, sandwich, NeuralNetTools,
Packaged: 2019-10-16 11:55:23 UTC; julioemilio

Imports: xts, zoo, outliers, nnet, pracma, sandwich
Description: Chaos theory has been hailed as a revolution of thoughts and attracting ever increasing
attention of many scientists from diverse disciplines. Chaotic systems are nonlinear deterministic
dynamic systems which can behave like an erratic and apparently random motion. A relevant field
inside chaos theory and nonlinear time series analysis is the detection of a chaotic behaviour
from empirical time series data. One of the main features of chaos is the well known initial value
sensitivity property. Methods and techniques related to test the hypothesis of chaos try to quantify
the initial value sensitive property estimating the Lyapunov exponents. The DChaos package
provides different useful tools and efficient algorithms which test robustly the hypothesis of chaos
based on the Lyapunov exponent in order to know if the data generating process behind time series
Packaged: 2020-05-04 18:16:08 UTC; julioemilioair