Search Results for author: Ben D. Fulcher

Found 12 papers, 8 papers with code

Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification

no code implementations31 Mar 2023 Trent Henderson, Annie G. Bryant, Ben D. Fulcher

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.

Classification Time Series Classification

Feature-Based Time-Series Analysis in R using the theft Package

1 code implementation12 Aug 2022 Trent Henderson, Ben D. Fulcher

With an increasing volume and complexity of time-series datasets in the sciences and industry, theft provides a standardized framework for comprehensively quantifying and interpreting informative structure in time series.

Data Visualization Time Series Analysis +1

Unifying Pairwise Interactions in Complex Dynamics

1 code implementation28 Jan 2022 Oliver M. Cliff, Joseph T. Lizier, Naotsugu Tsuchiya, Ben D. Fulcher

Scientists have developed hundreds of techniques to measure the interactions between pairs of processes in complex systems.

Causal Inference Time Series Analysis

An Empirical Evaluation of Time-Series Feature Sets

1 code implementation21 Oct 2021 Trent Henderson, Ben D. Fulcher

For example, in TSFEL, 90% of the variance across 390 features can be captured with just four PCs.

Time Series Analysis

Assessing the Significance of Directed and Multivariate Measures of Linear Dependence Between Time Series

1 code implementation9 Mar 2020 Oliver M. Cliff, Leonardo Novelli, Ben D. Fulcher, James M. Shine, Joseph T. Lizier

Inferring linear dependence between time series is central to our understanding of natural and artificial systems.

Methodology Information Theory Information Theory Statistics Theory Data Analysis, Statistics and Probability Neurons and Cognition Applications Statistics Theory

CompEngine: a self-organizing, living library of time-series data

1 code implementation3 May 2019 Ben D. Fulcher, Carl H. Lubba, Sarab S. Sethi, Nick S. Jones

Modern biomedical applications often involve time-series data, from high-throughput phenotyping of model organisms, through to individual disease diagnosis and treatment using biomedical data streams.

Databases Data Analysis, Statistics and Probability

catch22: CAnonical Time-series CHaracteristics

3 code implementations29 Jan 2019 Carl H. Lubba, Sarab S. Sethi, Philip Knaute, Simon R Schultz, Ben D. Fulcher, Nick S. Jones

Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry.

Classification Dimensionality Reduction +3

hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction

1 code implementation Cell Systems 2017 Ben D. Fulcher, Nick S. Jones

Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis.

Time Series Analysis

Feature-based time-series analysis

no code implementations23 Sep 2017 Ben D. Fulcher

This work presents an introduction to feature-based time-series analysis.

Time Series Analysis

Automatic time-series phenotyping using massive feature extraction

no code implementations15 Dec 2016 Ben D. Fulcher, Nick S. Jones

Across a far-reaching diversity of scientific and industrial applications, a general key problem involves relating the structure of time-series data to a meaningful outcome, such as detecting anomalous events from sensor recordings, or diagnosing patients from physiological time-series measurements like heart rate or brain activity.

Time Series Analysis

Highly comparative feature-based time-series classification

no code implementations15 Jan 2014 Ben D. Fulcher, Nick S. Jones

A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series.

Classification Dimensionality Reduction +5

Highly comparative time-series analysis: The empirical structure of time series and their methods

1 code implementation Journal of the Royal Society Interface 2013 Ben D. Fulcher, Max A. Little, Nick S. Jones

This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines, and automate the selection of useful methods for time-series classification and regression tasks.

Time Series Analysis Time Series Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.