- Generating time series
- Linear tools
- Utilities
- Stationarity
- Embedding and Poincaré sections
- Prediction
- Noise reduction
- Dimension and entropy estimation
- Lyapunov exponents
- Surrogate data
- Spike trains
- XTisean
- Unsupported

Create Hénon time series | henon |

Create Ikeda time series | ikeda |

Create Lorenz time series | lorenz |

Run an autoregressive model | ar-run |

Add noise to data | makenoise |

AR model | ar-model, ar-run |

ARIMA model | arima-model |

Autocorrelation function | corr |

Power spectrum using the maximum entropy method | mem_spec |

Power spectrum using FFT | spectrum |

Principal Component Analysis | pca |

Notch filter | notch |

Wiener filter | wiener |

Simple low pass filter | low121 |

Savitzky-Golay filter | sav_gol |

Choose sub-sequence or columns | choose |

Normalise, rescale, mean, standard deviation | rescale, rms |

Distribution of the data | histogram |

Change sampling time | resample |

There is a short corresponding section in the introduction paper.

Recurrence plot | recurr |

Space-time separation plot | stp |

Stationarity test | nstat_z |

Phase space reconstruction is discussed also in the the introduction paper.

Embed using delay coordinates | delay |

Mutual information of the data | mutual |

Poincaré section | poincare |

Determine the extrema of a time series | extrema |

Unstable periodic orbits | upo, upoembed |

False nearest neighbours | false_nearest |

For a discussion of these methods and examples see the corresponding section of the introduction paper.

Locally zeroth order model test | lzo-test |

Iterate locally zeroth order model | lzo-run |

Locally first order model test | lfo-test |

Iterate locally first order model | lfo-run |

Local vs. global linear prediction | lfo-ar |

Local vs. global mean prediction | lzo-gm |

Radial basis function fit | rbf |

Polynomial model | polynom, polynomp, polyback, polypar |

The introduction paper has a section on nonlinear noise reduction, too.

Add noise to data | makenoise |

Compare two data sets | compare |

Simple nonlinear noise reduction | lazy |

Nonlinear noise reduction | ghkss |

There is an implementation of the Grassbeger-Procaccia correlation integral in this package that can handle multivariate data and mixed embeddings. A fixed mass algorithm for the information dimension D1 is available which also can handle multivariate data and mixed embeddings, and a box-counting implementation of the order Q Renyi entropies for multifractal studies.

Post-processing can be performed on the output in order to obtain Takens' estimator or the Gaussian kernel correlation integral, or just for smoothing.

You may want to consult the introduction paper for initial material on dimension estimation. If you are serious, you will need to study some of the literature cited there as well.

Correlation dimension d2 | d2 |

Fixed mass estimation of D1 | c1 |

Renyi Entropies of Qth order | boxcount |

Takens estimator | c2t |

Gaussian kernel C2 | c2g |

Simply smooth the output of d2 | av-d2 |

Get local slopes from the correlation integral | c2d |

The estimation of Lyapunov exponents is also discussed in the introduction paper. A recent addition is a programm to compute finite time exponents which are not invariant but contain additional information.

Maximal exponent | lyap_k, lyap_r |

Lyapunov spectrum | lyap_spec |

There is a short overview page for nonlinearity tests. There is also a section in the introduction paper.

Make surrogate data | surrogates |

Determine end-to-end mismatch | endtoend |

General constrained randomization | randomize |

Discriminating statistics | timerev, predict |

Event/intervcal conversion | intervals |

Interval/event conversion | events |

Autocorrelation function of event times | spikeauto |

Power spectrum of event times | spikespec |

Surrogate data preserving event time autocorrelations | randomize_spikeauto_exp_random |

Surrogate data preserving event time power spectrum | randomize_spikespec_exp_event |

Since at least two time series are involved in these programs the usage
of some flags is different in case that the programs deal with
multivariate data.

The -m or -M refer to
the columns to be loaded for each data set. Thus, -m
2,2 means two colums for each data set. In combination with
-c this requires to specify twice as many
columns to this flag as are given with -m[M].

Linear cross-correlations | xcor |

Nonlinear cross-prediction | xzero |

Cross-correlation integral | xc2 |

Cross-recurrence Plot | xrecur |

Fortran version of delay embedding | delay |

Add noise to data | addnoise |

Autocorrelation function | autocor |

Principal component analysis | pc |

Simple nonlinear noise reduction | nrlazy |

Nonlinear noise reduction | project |

Naive implementation of the correlation dimension | c2naive |

Finite size exponents | fsle |