This program takes two data sets (i.e. two columns in a single datafile) and fits a zeroth order model of data set 1 to predict data set 2 (cross prediction). It then computes the error of the model. This is done by searching for all neighbors in set 1 of the points of set 2 which should be forecasted and taking as their images the average of the images of the neighbors. The given forecast error is normalized to the variance of data set 2.

Everything not being a valid option will be interpreted as a potential datafile name. Given no datafile at all, means read stdin. Also - means stdin

Possible options are:

Option | Description | Default |
---|---|---|

-l# | number of points to use | whole file |

-x# | number of lines to be ignored | 0 |

-c# | columns to be read (separated by a comma) | 1,2 |

-m# | embedding dimension | 3 |

-d# | delay for the embedding | 1 |

-n# | for how many points should the error be calculated | all |

-k# | minimal numbers of neighbors for the fit | 30 |

-r# | neighborhood size to start with | (data interval)/1000 |

-f# | factor to increase the neighborhood size if not enough neighbors were found | 1.2 |

-s# | steps to be forecasted (x^{2}_{n+steps}=
av(x^{1}_{i+steps})
| 1 |

-o# | output file name | without file name: 'datafile'.cze (or stdin.cze if stdin was read) If no -o is given stdout is used |

-V# | verbosity level 0: only panic messages 1: add input/output messages | 1 |

-h | show these options | none |

View the C-sources.

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