Package: tsfeatures 1.1.1.9000

tsfeatures: Time Series Feature Extraction

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.

Authors:Rob Hyndman [aut, cre], Yanfei Kang [aut], Pablo Montero-Manso [aut], Mitchell O'Hara-Wild [aut], Thiyanga Talagala [aut], Earo Wang [aut], Yangzhuoran Yang [aut], Souhaib Ben Taieb [ctb], Cao Hanqing [ctb], D K Lake [ctb], Nikolay Laptev [ctb], J R Moorman [ctb], Bohan Zhang [ctb]

tsfeatures_1.1.1.9000.tar.gz
tsfeatures_1.1.1.9000.zip(r-4.5)tsfeatures_1.1.1.9000.zip(r-4.4)tsfeatures_1.1.1.9000.zip(r-4.3)
tsfeatures_1.1.1.9000.tgz(r-4.4-any)tsfeatures_1.1.1.9000.tgz(r-4.3-any)
tsfeatures_1.1.1.9000.tar.gz(r-4.5-noble)tsfeatures_1.1.1.9000.tar.gz(r-4.4-noble)
tsfeatures_1.1.1.9000.tgz(r-4.4-emscripten)tsfeatures_1.1.1.9000.tgz(r-4.3-emscripten)
tsfeatures.pdf |tsfeatures.html✨
tsfeatures/json (API)
NEWS

# Install 'tsfeatures' in R:
install.packages('tsfeatures', repos = c('https://robjhyndman.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/robjhyndman/tsfeatures/issues

On CRAN:

feature-extractiontime-series

11.66 score 252 stars 21 packages 266 scripts 22k downloads 44 exports 54 dependencies

Last updated 4 months agofrom:dfaff35091. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 21 2024
R-4.5-winOKOct 21 2024
R-4.5-linuxOKOct 21 2024
R-4.4-winOKOct 21 2024
R-4.4-macOKOct 21 2024
R-4.3-winOKOct 21 2024
R-4.3-macOKOct 21 2024

Exports:ac_9acf_featuresarch_statautocorr_featuresbinarize_meancompenginecrossing_pointsdist_featuresembed2_incircleentropyfirstmin_acfirstzero_acflat_spotsfluctanal_prop_r1heterogeneityhistogram_modeholt_parametershursthw_parameterslocalsimple_taureslumpinessmax_kl_shiftmax_level_shiftmax_var_shiftmotiftwo_entro3nonlinearityoutlierinclude_mdrmdpacf_featurespred_featuressampen_firstsampencscal_featuresspreadrandomlocal_meantaulstabilitystation_featuresstd1st_derstl_featurestrev_numtsfeaturesunitroot_kpssunitroot_ppwalker_propcrossyahoo_datazero_proportion

Dependencies:clicodetoolscolorspacecurldigestfansifarverforecastfracdifffurrrfuturegenericsggplot2globalsgluegtableisobandjsonlitelabelinglatticelifecyclelistenvlmtestmagrittrMASSMatrixmgcvmunsellnlmennetparallellypillarpkgconfigpurrrquadprogquantmodR6RColorBrewerRcppRcppArmadilloRcppRollrlangscalestibbletimeDatetseriesTTRurcautf8vctrsviridisLitewithrxtszoo

Introduction to the tsfeatures package

Yangzhuoran Yang and Rob J Hyndman

Rendered fromtsfeatures.Rmdusingknitr::rmarkdownon Oct 21 2024.

Last update: 2023-08-28
Started: 2018-10-24

Readme and manuals

Help Manual

Help pageTopics
Autocorrelation at lag 9. Included for completion and consistency.ac_9
Autocorrelation-based featuresacf_features
ARCH LM Statisticarch_stat
Convert mts object to list of time seriesas.list.mts
The autocorrelation feature set from software package 'hctsa'autocorr_features
Converts an input vector into a binarized version from software package 'hctsa'binarize_mean
CompEngine feature setcompengine
Number of crossing pointscrossing_points
The distribution feature set from software package 'hctsa'dist_features
Points inside a given circular boundary in a 2-d embedding space from software package 'hctsa'embed2_incircle
Spectral entropy of a time seriesentropy
Time of first minimum in the autocorrelation function from software package 'hctsa'firstmin_ac
The first zero crossing of the autocorrelation function from software package 'hctsa'firstzero_ac
Longest flat spotflat_spots
Implements fluctuation analysis from software package 'hctsa'fluctanal_prop_r1
Heterogeneity coefficientsheterogeneity
Mode of a data vector from software package 'hctsa'histogram_mode
Parameter estimates of Holt's linear trend methodholt_parameters hw_parameters
Hurst coefficienthurst
The first zero crossing of the autocorrelation function of the residuals from Simple local time-series forecasting from software package 'hctsa'localsimple_taures
Time series features based on tiled windowslumpiness stability
Time series features based on sliding windowsmax_kl_shift max_level_shift max_var_shift
Local motifs in a binary symbolization of the time series from software package 'hctsa'motiftwo_entro3
Nonlinearity coefficientnonlinearity
How median depend on distributional outliers from software package 'hctsa'outlierinclude_mdrmd
Partial autocorrelation-based featurespacf_features
The prediction feature set from software package 'hctsa'pred_features
Second Sample Entropy of a time series from software package 'hctsa'sampen_first
Second Sample Entropy from software package 'hctsa'sampenc
The scaling feature set from software package 'hctsa'scal_features
Bootstrap-based stationarity measure from software package 'hctsa'spreadrandomlocal_meantaul
The stationarity feature set from software package 'hctsa'station_features
Standard deviation of the first derivative of the time series from software package 'hctsa'std1st_der
Strength of trend and seasonality of a time seriesstl_features
Normalized nonlinear autocorrelation, the numerator of the trev function of a time series from software package 'hctsa'trev_num
Time series feature matrixtsfeatures
Unit Root Test Statisticsunitroot_kpss unitroot_pp
Simulates a hypothetical walker moving through the time domain from software package 'hctsa'walker_propcross
Yahoo server metricsyahoo_data
Proportion of zeroszero_proportion