te_cont

Models for non-linear time series analysis and causality detection. The main functionalities of this package consist of an implementation of the classical causality test (C.W.J.Granger 1980) <doi:10.1016/0165-1889(80)90069-X>, and a non-linear version of it based on feed-forward neural networks. This package contains also an implementation of the Transfer Entropy <doi:10.1103/PhysRevLett.85.461>, and the continuous Transfer Entropy using an approximation based on the k-nearest neighbors <doi:10.1103/PhysRevE.69.066138>. There are also some other useful tools, like the VARNN (Vector Auto-Regressive Neural Network) prediction model, the Augmented test of stationarity, and the discrete and continuous entropy and mutual information.

Youssef Hmamouche

NlinTS

Models for Non Linear Causality Detection in Time Series

te_cont function

<dl><dt>X</dt>
<dd>Integer vector, first time series.</dd>
<dt>Y</dt>
<dd>Integer vector, the second time series.</dd>
<dt>p</dt>
<dd>Integer, the lag parameter to use for the first vector, (p = 1 by default).</dd>
<dt>q</dt>
<dd>Integer the lag parameter to use for the first vector, (q = 1 by default).</dd>
<dt>k</dt>
<dd>Integer argument, the number of neighbors.</dd>
<dt>normalize</dt>
<dd>Logical argument for the option of normalizing value of TE (transfer entropy) (FALSE by default).
This normalization is different from the discrete case, because, here the term H (X(t)| X(t-1), ..., X(t-p)) may be negative.
Consequently, we use another technique, we divide TE by H0 - H (X(t)| X(t-1), ..., X(t-p), Yt-1), ..., Y(t-q)), where H0 is the max entropy (of uniform distribution).</dd></dl>

Arguments

Continuous  Transfer Entropy — te_cont

<dl>

<dt>X</dt>
<dd>Integer vector, first time series.</dd>


<dt>Y</dt>
<dd>Integer vector, the second time series.</dd>


<dt>p</dt>
<dd>Integer, the lag parameter to use for the first vector, (p = 1 by default).</dd>


<dt>q</dt>
<dd>Integer the lag parameter to use for the first vector, (q = 1 by default).</dd>


<dt>k</dt>
<dd>Integer argument, the number of neighbors.</dd>


<dt>normalize</dt>
<dd>Logical argument for the option of normalizing value of TE (transfer entropy) (FALSE by default).
This normalization is different from the discrete case, because, here the term H (X(t)| X(t-1), ..., X(t-p)) may be negative.
Consequently, we use another technique, we divide TE by H0 - H (X(t)| X(t-1), ..., X(t-p), Yt-1), ..., Y(t-q)), where H0 is the max entropy (of uniform distribution).</dd>

</dl>

te_cont: Continuous Transfer Entropy

Description

Usage

Arguments

Details

References

Examples