Chaotic Time Series Analysis
Provides the delayed-coordinate embedding vectors backwards
Simulates time-series data from the Gauss map
Simulates time-series data from the Henon map
Computes the partial derivatives from the best-fitted neural net model
Simulates time-series data from the Logistic map
Estimates the largest Lyapunov exponent
Estimates the Lyapunov exponent through several methods
Estimates the Lyapunov exponent spectrum
Fits any standard feedforward neural net model from time-series data
Simulates time-series data from the Rossler system
Summary method for a lyapunov object
Estimates the initial parameter vector of the neural net model
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 behave chaotically or not.