Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the …
CK Assaad, E Devijver, E Gaussier - Journal of Artificial Intelligence …, 2022 - jair.org
We introduce in this survey the major concepts, models, and algorithms proposed so far to infer causal relations from observational time series, a task usually referred to as causal …
In the last decade, there has been a growing body of literature addressing the utilization of complex network methods for the characterization of dynamical systems based on time …
Z Jin, Y Yang, Y Liu - Neural Computing and Applications, 2020 - Springer
Stock market prediction has been identified as a very important practical problem in the economic field. However, the timely prediction of the market is generally regarded as one of …
Oscillatory neuronal activity may provide a mechanism for dynamic network coordination. Rhythmic neuronal interactions can be quantified using multiple metrics, each with their own …
In predictive coding, experience generates predictions that attenuate the feeding forward of predicted stimuli while passing forward unpredicted “errors.” Different models have …
Time series data are a collection of chronological observations which are generated by several domains such as medical and financial fields. Over the years, different tasks such as …
L Barnett, AK Seth - Journal of neuroscience methods, 2014 - Elsevier
Abstract Background Wiener–Granger causality (“G-causality”) is a statistical notion of causality applicable to time series data, whereby cause precedes, and helps predict, effect. It …
There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data …