tisean
Port of TISEAN 3.0.1
Select category:
Generating time series
Linear tools
Phase space representation
Nonlinear noise reduction
Nonlinear prediction
Lyapunov exponents
Dimensions and entropies
Testing for nonlinearity
Spike trains
Generate Henon map
Generate Ikeda map
Produce delay vectors
Produce delay vectors
Determines the fraction of false nearest neighbors.
Estimates the time delayed mutual information of the data.
Performs a global principal component analysis (PCA).
Make a Poincare section for time continuous scalar data sets along one of the coordinates of the embedding vector.
Performs simple nonlinear noise reduction
Multivariate noise reduction using the GHKSS algorithm.
Locates unstable periodic points.
Creates delay coordinates for upo output.
Estimates the average forecast error for a local constant (zeroth order) fit as a function of the neighborhood size.
This program fits a locally zeroth order model to a possibly multivariate time series and iterates the time series into the future.
Estimates the average forecast error for a zeroth order fit from a multidimensional time series
This program makes a local linear ansatz and estimates the one step prediction error of the model.
This function depending on whether switch 'zeroth' is set produces either a local linear ansatz or a zeroth order ansatz for a possibly multivariate time series and iterates an artificial trajectory.
Makes a local linear ansatz and estimates the one step prediction error of the model.
Models the data making a polynomial ansatz.
This program models the data using a radial basis function (rbf) ansatz.
Takes two data sets and fits a zeroth order model of data set 1 (X1) to predict data set 2 (X2) - cross prediction.
Estimates the maximum Lyapunov exponent using the algorithm described by Kantz on the TISEAN reference page:
Estimates the largest Lyapunov exponent of a given scalar data set using the algorithm described by Resentein et al.
Estimates the spectrum of Lyapunov exponents using the method of Sano and Sawada.
This program estimates the correlation sum, the correlation dimension and the correlation entropy of a given, possibly multivariate, data set.
This program takes the output of d2, c2d or c1 and smooths it by averaging over a given interval.
This program calculates the maximum likelihood estimator (the Takens' estimator) from correlation sums of the output of d2 (the 'c2' field of the d2 output) or c1 (the 'c1' field of c1 output).
This program calculates the Gaussian kernel correlation integral and its logarithmic derivatice from correlation sums calculated by d2 (the 'c2' field of the d2 output).
Computers curves for the fixed mass computation of information dimension (mentioned in TISEAN 3.0.1 documentation).
This program calculates the local slopes by fitting straight lines onto c1 correlation sum data (the 'c1' field of the c1 output).
Estimates the Renyi entropy of Qth order using a partition of the phase space instead of using the Grassberger-Procaccia scheme.
Generates multivariate surrogate data (implements the iterative Fourier scheme).
Determine the effect of an end-to-end mismatch on the autocorrelation structure for various sub-sequence lengths.
Calculates time reversal assymetry statistic.
Computes the binned autocorrelation function of a series of event times.
Computes a power spectrum assuming that the data are the times of singular events, e.g.
Package: tisean
