theft - Tools for Handling Extraction of Features from Time Series

Consolidates and calculates different sets of time-series features from multiple 'R' and 'Python' packages including 'Rcatch22' Henderson, T. (2021) <doi:10.5281/zenodo.5546815>, 'feasts' O'Hara-Wild, M., Hyndman, R., and Wang, E. (2021) <https://CRAN.R-project.org/package=feasts>, 'tsfeatures' Hyndman, R., Kang, Y., Montero-Manso, P., Talagala, T., Wang, E., Yang, Y., and O'Hara-Wild, M. (2020) <https://CRAN.R-project.org/package=tsfeatures>, 'tsfresh' Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr A.W. (2018) <doi:10.1016/j.neucom.2018.03.067>, 'TSFEL' Barandas, M., et al. (2020) <doi:10.1016/j.softx.2020.100456>, and 'Kats' Facebook Infrastructure Data Science (2021) <https://facebookresearch.github.io/Kats/>.

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feature-extractionmachine-learningtime-series

7.49 score 43 stars 1 dependents 48 scripts 582 downloads

Rcatch22 - Calculation of 22 CAnonical Time-Series CHaracteristics

Calculate 22 summary statistics coded in C on time-series vectors to enable pattern detection, classification, and regression applications in the feature space as proposed by Lubba et al. (2019) <doi:10.1007/s10618-019-00647-x>.

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machine-learningtime-seriescpp

6.74 score 22 stars 2 dependents 28 scripts 343 downloads

correctR - Corrected Test Statistics for Comparing Machine Learning Models on Correlated Samples

Calculate a set of corrected test statistics for cases when samples are not independent, such as when classification accuracy values are obtained over resamples or through k-fold cross-validation, as proposed by Nadeau and Bengio (2003) <doi:10.1023/A:1024068626366> and presented in Bouckaert and Frank (2004) <doi:10.1007/978-3-540-24775-3_3>.

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hypothesis-testingmachine-learningstatistics

5.62 score 25 stars 1 dependents 11 scripts 337 downloads

theftdlc - Analyse and Interpret Time Series Features

Provides a suite of functions for analysing, interpreting, and visualising time-series features calculated from different feature sets from the 'theft' package. Implements statistical learning methodologies described in Henderson, T., Bryant, A., and Fulcher, B. (2023) <doi:10.48550/arXiv.2303.17809>.

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data-sciencedata-visualizationmachine-learningstatisticstime-series

5.33 score 5 stars 17 scripts 194 downloads

normaliseR - Re-Scale Vectors and Time-Series Features

Provides standardized access to a range of re-scaling methods for numerical vectors and time-series features calculated within the 'theft' ecosystem.

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4.18 score 1 dependents 656 downloads