Quantifying causality between variables from observed time series data is of great importance in various disciplines but also a challenging task, especially when the observed data are short. Unlike the conventional methods, we find it possible to detect causality only with very short time series data, based on embedding theory of an attractor for nonlinear dynamics. Specifically, we first show ...

The causation relationship between economic growth and military expenditures in developing countries has received growing focus in recent years. One of the arguments in this field is that military spending absorbs a significant proportion of the limited financial resources in LDCs. The present study examines the causal relation in two models: defense spending with total real economic growth and...

Probably the most fundamental problem is the origin of time and causality. The inherent difficulty is that all scientific theories of origins and evolution consider the existence of time and causality as given. We tackle this problem by starting from the concept of self-organization, which is seen as the spontaneous emergence of order out of primordial chaos. Self-organization can be explained ...

We present an improvement of an estimator of causality in financial time series via transfer entropy, which includes the side information that may affect the cause-effect relation in the system, i.e. a conditional information-transfer based causality. We show that for weakly stationary time series the conditional transfer entropy measure is nonnegative and bounded below by the Geweke’s measure ...

Mutual interactions between the agricultural commodities futures market and spot are some of most important relationships that can be observed financial real economy. The process flow price impulses these markets has long been interest to researchers. It is both for stock investors producers, as well decision-makers responsible food policy. studies conducted so far do not provide unambiguous an...

Abstract We proposed a method to extract causal relations of spatial clusters from multi-dimensional event sequence data, also known as spatio-temporal point process. The Granger cluster mining algorithm identifies the pairs data that have causality over time with each other. It extended algorithm, which utilized statistical inference technique identify occurrence relation, based on causality. ...

in Distributed Programs Ashis Tarafdar Vijay K. Garg Dept. of Computer Sciences Dept. of Electrical and Computer Engg. University of Texas at Austin University of Texas at Austin Austin, TX 78712 Austin, TX 78712 ([email protected]) ([email protected]) Abstract The partial-order model of distributed computations based on the happened before relation has been criticized for allowing false ca...

In parallel discrete event simulation, a run in which events are executed at each object according to their timestamp is a correct run (i.e., it satisses causal-ity). In such a run, the execution order of events at any object corresponds to the total order imposed on them by timestamps. In this paper we introduce the notion of weak causality between simulation events. Such a relation deenes a p...