This paper uses the smooth transition regression (STR) model to study the nonlinear relation between the consumer price index (CPI) and the composite index of Shanghai Stock Market in China from 1999 to 2011. The results show that there exists one way Granger causality relation from CPI to stock market in China; the internal relation between the CPI and stock market’s composite index appears as...

The inference of the couplings of an Ising model with given means and correlations is called inverse Ising problem. This approach has received a lot of attention as a tool to analyze neural data. We show that autoregressive methods may be used to learn the couplings of an Ising model, also in the case of asymmetric connections and for multi-spin interactions. We find that, for each link, the li...

Selecting relevant features in mass spectra analysis is important both for classification and search for causality. In this paper, it is shown how using mutual information can help answering to both objectives, in a model-free nonlinear way. A combination of ranking and forward selection makes it possible to select several feature groups that may lead to similar classification performances, but...

[1] Based on the nonlinear techniques for estimation of coupling between oscillatory systems, we investigate the dynamics of climatic modes characterizing global and the Northern Hemisphere (NH) processes. In particular, indices of the North Atlantic Oscillation (NAO) and El Niño–Southern Oscillation (ENSO) for 1950–2004 are analyzed. The methods based on phase dynamics modeling and nonlinear ‘...

Inference of causality is central in nonlinear time series analysis and science in general. A popular approach to infer causality between two processes is to measure the information flow between them in terms of transfer entropy. Using dynamics of coupled oscillator networks, we show that although transfer entropy can successfully detect information flow in two processes, it often results in er...

It is generally believed that a major cause of motor dysfunction is the impairment in neural network that controls movement. But little is known about the underlying mechanisms of the impairment in cortical control or in the neural connections between cortex and muscle that lead to the loss of motor ability. So understanding the functional connection between motor cortex and effector muscle is ...

In this paper we present an approach to analyze the direction of information flow between time series involving bidirectional relations. The intuitive idea comes from a first study dedicated to the so-called phase slope index, which is a measure originally developed to detect unidirectional relations and is based on the complex coherence function. In order to detect bidirectional flows, we prop...

We consider causally sufficient acyclic causal models in which the relationship among the variables is nonlinear while disturbances have linear effects, and show that three principles, namely, the causal Markov condition (together with the independence between each disturbance and the corresponding parents), minimum disturbance entropy, and mutual independence of the disturbances, are equivalen...

Curie symmetry principle, the causality relation between the symmetry of the causes and the resultant effect, has often been invoked to infer the composite deformation history of geological bodies in the Earth’s crust. This principle provides a powerful constraint for predicting an unavailable past physical condition; the effects at the macroscopic level must be the same or higher symmetry than...

We provide empirical evidence that suggests social media and stock markets have a nonlinear causal relationship. We take advantage of an extensive data set composed of social media messages related to DJIA index components. By using information-theoretic measures to cope for possible nonlinear causal coupling between social media and stock markets systems, we point out stunning differences in t...