Models for time series forecasting and causality detection.  The main functionalities of this package consist of a neural network Vector Auto-Regressive model, 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.

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 Augmented test of stationarity, and the discrete and continuous entropy and mutual information.

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.

Models for Non Linear Causality Detection in Time Series
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.