Search Results for author:
Found 12 papers, 8 papers with code
Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification
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.
Feature-Based Time-Series Analysis in R using the theft Package
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.
Unifying Pairwise Interactions in Complex Dynamics
Scientists have developed hundreds of techniques to measure the interactions between pairs of processes in complex systems.
An Empirical Evaluation of Time-Series Feature Sets
For example, in TSFEL, 90% of the variance across 390 features can be captured with just four PCs.
Assessing the Significance of Directed and Multivariate Measures of Linear Dependence Between Time Series
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
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
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.
hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction
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.
Feature-based time-series analysis
This work presents an introduction to feature-based time-series analysis.
Automatic time-series phenotyping using massive feature extraction
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.
Highly comparative feature-based time-series classification
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.
Highly comparative time-series analysis: The empirical structure of time series and their methods
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.
