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− | + | In reference to the TISEAN library alphabetical order of programs which is located [http://www.mpipks-dresden.mpg.de/~tisean/Tisean_3.0.1/docs/alphabetical.html| here]. | |

− | In reference to the TISEAN library alphabetical order of programs which is located [http://www.mpipks-dresden.mpg.de/~tisean/Tisean_3.0.1/docs/alphabetical.html here]. | ||

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{| class="wikitable" | {| class="wikitable" | ||

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− | | | + | ! Program Name !! Program Description !! Corresponding Octave Function !! Comments |

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− | + | | arima-model || Fit and possibly iterate an ARIMA model || There is 'aar' in TSA but cannot determine if this is different or not || This is a c-file that can be wrapped in C++/mfile/octfile code | |

− | + | |- | |

+ | |ar-model || Fit and possibly iterate an Autoregessive model || Same as above || C; see also: aarmam, adim, amarma, mvaar from TSA | ||

+ | |- | ||

+ | |ar-run || Iterate an Autoregessive model || Same as above || FORTRAN | ||

+ | |- | ||

+ | |av-d2 || Simply smooth output of d2 || Same as above || C | ||

+ | |- | ||

+ | |boxcount || Renyi Entopies of Qth order || There most likely is none || C | ||

+ | |- | ||

+ | |c1 || Fixed mass estimation of D1 || Most likely is none || FORTRAN | ||

+ | |- | ||

+ | |c2d || Get local slopes from correlation integral || Most likely none || FORTRAN | ||

+ | |- | ||

+ | |c2g || Gaussian kernel of C2 || || | ||

+ | |- | ||

+ | |c2t || Takens estimator of D2 || Most likely 'rmle' from tsa || FORTRAN | ||

+ | |- | ||

+ | |choose || Choose rows and/or columns from a data file || Does not need to be ported || ------ | ||

+ | |- | ||

+ | |compare || Compares two data sets || If 'rms' exists in Octave no need for port || FORTRAN | ||

+ | |- | ||

+ | |corr, autocorr || Autocorrelation function || There is 'acorf' in tsa but i don't know if is the same || corr -C, autocorr (faster according to documentation) - FORTRAN | ||

+ | |- | ||

+ | |d2 || Correlation dimension d2 || I believe not, don't know || c | ||

+ | |- | ||

+ | |delay || Creates delay embedding || Most likely does not exist || C | ||

+ | |- | ||

+ | |endtoend || Determine end-to-end mismatch || || | ||

+ | |- | ||

+ | |events || Interval/event conversion || || | ||

+ | |- | ||

+ | |extrema || Determine the extrema of a time series || || | ||

+ | |- | ||

+ | |false_nearest || The false nearest neighbor algorithm || Does not exist || C | ||

+ | |- | ||

+ | |ghkss || Nonlinear noise reduction || There is not || C | ||

+ | |- | ||

+ | |henon || Create a Hénon time series || There is not || To m-file; already ported | ||

+ | |- | ||

+ | |histogram || Creates histograms || || | ||

+ | |- | ||

+ | |ikeda || Create an Ikeda time series || || | ||

+ | |- | ||

+ | |intervals || Event/intervcal conversion || || | ||

+ | |- | ||

+ | |lazy || Simple nonlinear noise reduction || There is not || FORTRAN | ||

+ | |- | ||

+ | |lfo-ar || Locally first order model vs. global AR model (old ll-ar) || || | ||

+ | |- | ||

+ | |lfo-run || Iterate a locally first order model (old nstep) || || | ||

+ | |- | ||

+ | |lfo-test || Test a locally first order model (old onestep) || || | ||

+ | |- | ||

+ | |lorenz || Create a Lorenz time series || || | ||

+ | |- | ||

+ | |low121 || Time domain low pass filter || || | ||

+ | |- | ||

+ | |lyap_k || Maximal Lyapunov exponent with the Kantz algorithm || || | ||

+ | |- | ||

+ | |lyap_r || Maximal Lyapunov exponent with the Rosenstein algorithm || || | ||

+ | |- | ||

+ | |lyap_spec || Full spectrum of Lyapunov exponents || || | ||

+ | |- | ||

+ | |lzo-gm || Locally zeroth order model vs. global mean || || | ||

+ | |- | ||

+ | |lzo-run || Iterate a locally zeroth order model || || | ||

+ | |- | ||

+ | |lzo-test || Test a locally zeroth order model (old zeroth) || || | ||

+ | |- | ||

+ | |makenoise || Produce noise || || | ||

+ | |- | ||

+ | |mem_spec || Power spectrum using the maximum entropy principle || || | ||

+ | |- | ||

+ | |mutual || Estimate the mutual information || Does not exist || C | ||

+ | |- | ||

+ | |notch || Notch filter || || | ||

+ | |- | ||

+ | |nstat_z || Nonstationarity testing via cross-prediction || || | ||

+ | |- | ||

+ | |pca, pc || Principle component analysis || 'pcacov' if likely the equivalent || pca - C, pc - FORTRAN | ||

+ | |- | ||

+ | |poincare || Create Poincaré sections || || | ||

+ | |- | ||

+ | |polyback || Fit a polynomial model (backward elimination) || || | ||

+ | |- | ||

+ | |polynom || Fit a polynomial model || || | ||

+ | |- | ||

+ | |polynomp || Fit a polynomial model (reads terms to fit from file) || || | ||

+ | |- | ||

+ | |polypar || Creates parameter file for polynomp || || | ||

+ | |- | ||

+ | |predict || Forecast discriminating statistics for surrogates || Does not exist || FORTRAN | ||

|- | |- | ||

− | + | |randomize || General constraint randomization (surrogates) || || | |

|- | |- | ||

− | | | + | |randomize_spikeauto_exp_random || Surrogate data preserving event time autocorrelations || || |

|- | |- | ||

− | | | + | |randomize_spikespec_exp_event || Surrogate data preserving event time power spectrum || || |

|- | |- | ||

− | | | + | |rbf || Radial basis functions fit || || |

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− | | | + | |recurr || Creates a recurrence plot || || |

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− | | | + | |resample || Resamples data || There is 'resample' in Octave, but I believe it does something else || C |

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− | | | + | |rescale || Rescale data set || This should be in Octave, cannot find... || C |

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− | | | + | |rms || Rescale data set and get mean, variance and data interval || This should be in Octave, cannot find... || FORTRNAN |

|- | |- | ||

− | | | + | |sav_gol || Savitzky-Golay filter || || |

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− | | | + | |spectrum || Power spectrum using FFT || || |

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− | | | + | |spikeauto || Autocorrelation function of event times || || |

|- | |- | ||

− | | | + | |spikespec || Power spectrum of event times || || |

|- | |- | ||

− | | | + | |stp || Creates a space-time separation plot || || |

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|- | |- | ||

− | | | + | |surrogates || Creates surrogate data || || |

|- | |- | ||

− | | | + | |timerev || Time reversal discrimating statistics for surrogates || || |

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− | | | + | |upo || Finds unstable periodic orbits and estimates their stability || No corresponding function in Octave || FORTRAN |

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− | | | + | |upoembed || Takes the output of upo and create data files out of it || || |

|- | |- | ||

− | | | + | |wiener || Wiener filter || || |

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− | | | + | |xc2 || Cross-correlation integral || || |

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|- | |- | ||

− | | | + | |xcor || Cross-correlations || || |

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− | | | + | |xrecur || Cross-recurrence Plot || || |

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|- | |- | ||

− | + | |xzero || Locally zeroth order cross-prediction | |

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− | |xzero || Locally zeroth order cross-prediction | ||

|} | |} |