# Description of the program: zeroth

This program makes a zeroth order ansatz and estimates the one step prediction error of the model. This is done by searching for all neighbors of the point to be forecasted and taking as its image the average of the images of the neighbors. The given forecast error is normalized to the variance of the data.

## Usage:

zeroth [Options]

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# | column to be read | 1
-m# | embedding dimension | 3
-d# | delay for the embedding | 1
-n# | for how many reference points should the error be calculated | all
-S# | what should the temporal distance between the reference points be | 1
-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 xn+steps=av(xi+steps) | 1
-o[#] | output file name | without file name: 'datafile'.zer (or stdin.zer 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

## Description of the Output

The output consists of s lines, each of which containing the steps forecasted (first column) and the relative forecast error (second column). Relative means that the forecast error is devided by the standard deviation of the data.

View the C-sources.

See also predict which does essentially the same but has some different options. The program mzeroth can handle multivariate data, and xzero does cross-predictions.

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