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DESCRIPTION
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DESCRIPTION
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Package: tsfeatures
Title: Time Series Feature Extraction
Version: 1.0.2.9014
Authors@R: c(
person("Rob", "Hyndman", email = "[email protected]", role = c("aut","cre"), comment = c(ORCID = "0000-0002-2140-5352")),
person("Yanfei", "Kang", role = "aut", comment = c(ORCID = "0000-0001-8769-6650")),
person("Pablo", "Montero-Manso", email="[email protected]", role="aut"),
person("Thiyanga", "Talagala", role = "aut", comment=c(ORCID = "0000-0002-0656-9789")),
person("Earo", "Wang", role = "aut", comment=c(ORCID = "0000-0001-6448-5260")),
person("Yangzhuoran", "Yang", email = "[email protected]", role = "aut"),
person("Mitchell", "O'Hara-Wild", role="aut", comment=c(ORCID = "0000-0001-6729-7695")),
person("Souhaib", "Ben Taieb", role = "ctb"),
person("Cao", "Hanqing", role="ctb"),
person("D K", "Lake", email="[email protected]", role="ctb"),
person("Nikolay", "Laptev", role="ctb"),
person("J R", "Moorman", role="ctb"))
Description: Methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) <doi:10.1109/ICDMW.2015.104>, Kang, Hyndman and Smith-Miles (2017) <doi:10.1016/j.ijforecast.2016.09.004> and from Fulcher, Little and Jones (2013) <doi:10.1098/rsif.2013.0048>. Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions.
Depends:
R (>= 3.6.0)
Imports:
fracdiff,
forecast (>= 8.3),
purrr,
RcppRoll (>= 0.2.2),
stats,
tibble,
tseries,
urca,
future,
furrr
Suggests:
testthat,
knitr,
rmarkdown,
ggplot2,
tidyr,
dplyr,
Mcomp,
GGally
License: GPL-3
LazyData: true
ByteCompile: true
URL: https://pkg.robjhyndman.com/tsfeatures/
BugReports: https://github.com/robjhyndman/tsfeatures/issues/
RoxygenNote: 7.1.0
Roxygen: list(markdown = TRUE, roclets=c('rd', 'collate', 'namespace'))
VignetteBuilder: knitr
Encoding: UTF-8