EvoTSC evolves lightweight feature learning models for time series classification via genetic programming with embedded expert knowledge and Pareto tournament selection, outperforming eleven benchmarks on univariate datasets.
hctsa: A computational framework for automated time-series phenotyping using massive feature extraction
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2representative citing papers
FreshPRINCE and DrCIF, two new unsupervised feature-based regressors adapted from time series classification, significantly outperform other methods on an expanded archive of 63 TSER problems and are the only ones to beat rotation forest by a statistically significant margin.
citing papers explorer
-
EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming
EvoTSC evolves lightweight feature learning models for time series classification via genetic programming with embedded expert knowledge and Pareto tournament selection, outperforming eleven benchmarks on univariate datasets.
-
Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression
FreshPRINCE and DrCIF, two new unsupervised feature-based regressors adapted from time series classification, significantly outperform other methods on an expanded archive of 63 TSER problems and are the only ones to beat rotation forest by a statistically significant margin.