3 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
Parameter inference from a non-stationary unknown process using statistical feature-based slow feature analysis
Kieran S. Owens, Masako Tamaki, Ben D. Fulcher
Non-stationary phenomena are ubiquitous, with examples to be found in climatological measurements, brain activity, and the behavior of financial markets. Starting with a time serie…
Interpretable model-free inference of parametric variation across time-series data through large-scale feature extraction
Ben D. Fulcher, Carl H. Lubba, Giorgio F. Gilestro +2
Here we address the problem of estimating the dimensionality and nature of parametric variation in an unknown generative process directly from time-series data, without specifying…
Identifying statistical indicators of temporal asymmetry using a data-driven approach
Teresa Dalle Nogare, Ben D. Fulcher
The dynamics of time-reversible systems are statistically indistinguishable when observed forward or backward in time. A rich literature of statistical methods to distinguish irrev…
Parameter inference from a non-stationary unknown process
Kieran S. Owens, Ben D. Fulcher
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…
A feature-based information-theoretic approach for detecting interpretable, long-timescale pairwise interactions from time series
Aria Nguyen, Oscar McMullin, Joseph T. Lizier +1
Quantifying relationships between components of a complex system is critical to understanding the rich network of interactions that characterize the behavior of the system. Traditi…