Zobrazeno 1 - 10
of 264
pro vyhledávání: '"Fulcher, Ben"'
Generative models of brain activity have been instrumental in testing hypothesized mechanisms underlying brain dynamics against experimental datasets. Beyond capturing the key mechanisms underlying spontaneous brain dynamics, these models hold an exc
Externí odkaz:
http://arxiv.org/abs/2407.19737
Autor:
Owens, Kieran S., Fulcher, Ben D.
Non-stationary systems are found throughout the world, from climate patterns under the influence of variation in carbon dioxide concentration, to brain dynamics driven by ascending neuromodulation. Accordingly, there is a need for methods to analyze
Externí odkaz:
http://arxiv.org/abs/2407.08987
Quantifying relationships between components of a complex system is critical to understanding the rich network of interactions that characterize the behavior of the system. Traditional methods for detecting pairwise dependence of time series, such as
Externí odkaz:
http://arxiv.org/abs/2404.05929
Many real-world systems undergo abrupt changes in dynamics as they move across critical points, often with dramatic consequences. Much existing theory on identifying the time-series signatures of nearby critical points -- such as increased variance a
Externí odkaz:
http://arxiv.org/abs/2310.14791
Autor:
Cajic, Pavle, Agius, Dominic, Cliff, Oliver M., Shine, James M., Lizier, Joseph T., Fulcher, Ben D.
The participation coefficient is a widely used metric of the diversity of a node's connections with respect to a modular partition of a network. An information-theoretic formulation of this concept of connection diversity, referred to here as partici
Externí odkaz:
http://arxiv.org/abs/2307.12556
The variety of complex algorithmic approaches for tackling time-series classification problems has grown considerably over the past decades, including the development of sophisticated but challenging-to-interpret deep-learning-based methods. But with
Externí odkaz:
http://arxiv.org/abs/2303.17809
Autor:
Henderson, Trent, Fulcher, Ben D.
Time series are measured and analyzed across the sciences. One method of quantifying the structure of time series is by calculating a set of summary statistics or `features', and then representing a time series in terms of its properties as a feature
Externí odkaz:
http://arxiv.org/abs/2208.06146
Autor:
Barbara, Nicholas H., Bedding, Timothy R., Fulcher, Ben D., Murphy, Simon J., Van Reeth, Timothy
With the availability of large-scale surveys like Kepler and TESS, there is a pressing need for automated methods to classify light curves according to known classes of variable stars. We introduce a new algorithm for classifying light curves that co
Externí odkaz:
http://arxiv.org/abs/2205.03020
Scientists have developed hundreds of techniques to measure the interactions between pairs of processes in complex systems. But these computational methods, from correlation coefficients to causal inference, rely on distinct quantitative theories tha
Externí odkaz:
http://arxiv.org/abs/2201.11941
Autor:
Henderson, Trent, Fulcher, Ben D.
Solving time-series problems with features has been rising in popularity due to the availability of software for feature extraction. Feature-based time-series analysis can now be performed using many different feature sets, including hctsa (7730 feat
Externí odkaz:
http://arxiv.org/abs/2110.10914