At present some of the most interesting scientific problems require investigating short, irregular, chaotic and sometimes corrupted time series. Identifying the mutual, causal influences between such signals is particularly challenging, particularly because in many instances interventions and experiments are difficult, expensive or utterly impossible. The conversion of time series into complex networks has recently become a very active area of research. The properties of the networks can be quantified with various tools, typically converting the adjacency map into an image before deploying image processing techniques. The proposed methods are exemplified with real time cases, ranging from atmospheric physics and epidemiology to thermonuclear fusion.
Complex Networks and Causality between Time Series
Murari A
2021
Abstract
At present some of the most interesting scientific problems require investigating short, irregular, chaotic and sometimes corrupted time series. Identifying the mutual, causal influences between such signals is particularly challenging, particularly because in many instances interventions and experiments are difficult, expensive or utterly impossible. The conversion of time series into complex networks has recently become a very active area of research. The properties of the networks can be quantified with various tools, typically converting the adjacency map into an image before deploying image processing techniques. The proposed methods are exemplified with real time cases, ranging from atmospheric physics and epidemiology to thermonuclear fusion.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
