| A neural network architecture is discussed which uses Finite Impulse Response (FIR) linear lters to provide dynamic interconnectivity between processing units. The network is applied to a variety of chaotic time series prediction tasks. Phase-space plots of the network dynamics are given to illustrate the reconstruction of underlying chaotic attractors. An example taken from the Santa Fe Inst...

In this work we summarize some of recent and classical results on the role played by copulas in the analysis of chaotic processes and univariate time series. We review some aspects of the copulas related to chaotic process, its properties and applications. We also present a review on classical and modern approaches to understand the relationship among random variables in Markov processes as wel...

This report describes a neural network architecture ClusNet designed for the prediction of chaotic time series. It advantages include simplicity, fast and sure convergence, and less need for computing resources. After describing its architecture and learning algorithms, its prediction perfo1:mance on the Logistic and the Mackay-Class chaotic time series is presented. Compared to other current p...