4 citations · 7 across the 3 of their papers we have counts for
8 papers
An Empirical Evaluation of Time-Series Feature Sets
Trent Henderson, Ben D. Fulcher
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 p…
Winning with Simple Learning Models: Detecting Earthquakes in Groningen, the Netherlands
Umair bin Waheed, Ahmed Shaheen, Mike Fehler +1
Deep learning is fast emerging as a potential disruptive tool to tackle longstanding research problems across the sciences. Notwithstanding its success across disciplines, the rece…
Finding binaries from phase modulation of pulsating stars with \textit{Kepler}: VI. Orbits for 10 new binaries with mischaracterised primaries
Simon J. Murphy, Nicholas H. Barbara, Daniel Hey +2
Measuring phase modulation in pulsating stars has proved to be a highly successful way of finding binary systems. The class of pulsating main-sequence A and F variables known as de…
Assessing the Significance of Directed and Multivariate Measures of Linear Dependence Between Time Series
Oliver M. Cliff, Leonardo Novelli, Ben D. Fulcher +2
Inferring linear dependence between time series is central to our understanding of natural and artificial systems. Unfortunately, the hypothesis tests that are used to determine st…
CompEngine: a self-organizing, living library of time-series data
Ben D. Fulcher, Carl H. Lubba, Sarab S. Sethi +1
Modern biomedical applications often involve time-series data, from high-throughput phenotyping of model organisms, through to individual disease diagnosis and treatment using biom…
catch22: CAnonical Time-series CHaracteristics
Carl H Lubba, Sarab S Sethi, Philip Knaute +3
Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across…