tsfeatures v1.0.1

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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.

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tsfeatures

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The R package tsfeatures provides methods for extracting various features from time series data.

Installation

You can install the stable version on R CRAN.

install.packages('tsfeatures', dependencies = TRUE)

You can install the development version from Github with:

# install.packages("devtools")
devtools::install_github("robjhyndman/tsfeatures")

Usage

library(tsfeatures)
mylist <- list(sunspot.year, WWWusage, AirPassengers, USAccDeaths)
myfeatures <- tsfeatures(mylist)
myfeatures
#> # A tibble: 4 x 20
#>   frequency nperiods seasonal_period trend   spike linearity curvature
#>       <dbl>    <dbl>           <dbl> <dbl>   <dbl>     <dbl>     <dbl>
#> 1         1        0               1 0.125 2.10e-5      3.58      1.11
#> 2         1        0               1 0.985 3.01e-8      4.45      1.10
#> 3        12        1              12 0.989 2.12e-8     11.0       1.10
#> 4        12        1              12 0.796 9.67e-7     -2.13      2.85
#> # ... with 13 more variables: e_acf1 <dbl>, e_acf10 <dbl>, entropy <dbl>,
#> #   x_acf1 <dbl>, x_acf10 <dbl>, diff1_acf1 <dbl>, diff1_acf10 <dbl>,
#> #   diff2_acf1 <dbl>, diff2_acf10 <dbl>, seasonal_strength <dbl>,
#> #   peak <dbl>, trough <dbl>, seas_acf1 <dbl>

License

This package is free and open source software, licensed under GPL-3.

Functions in tsfeatures

Name Description
hurst Hurst coefficient
ac_9 Autocorrelation at lag 9. Included for completion and consistency.
compengine CompEngine feature set
binarize_mean Converts an input vector into a binarized version from software package hctsa
acf_features Autocorrelation-based features
fluctanal_prop_r1 Implements fluctuation analysis from software package hctsa
entropy Spectral entropy of a time series
max_level_shift Time series features based on sliding windows
unitroot_kpss Unit Root Test Statistics
firstmin_ac Time of first minimum in the autocorrelation function from software package hctsa
crossing_points Number of crossing points
dist_features The distribution feature set from software package hctsa
embed2_incircle Points inside a given circular boundary in a 2-d embedding space from software package hctsa
pred_features The prediction feature set from software package hctsa
lumpiness Time series features based on tiled windows
sampen_first Second Sample Entropy of a time series from software package hctsa
firstzero_ac The first zero crossing of the autocorrelation function from software package hctsa
yahoo_data Yahoo server metrics
walker_propcross Simulates a hypothetical walker moving through the time domain from software package hctsa
heterogeneity Heterogeneity coefficients
sampenc Second Sample Entropy from software package hctsa
flat_spots Number of flat spots
histogram_mode Mode of a data vector from software package hctsa
holt_parameters Parameter estimates of Holt's linear trend method
motiftwo_entro3 Local motifs in a binary symbolization of the time series from software package hctsa
nonlinearity Nonlinearity coefficient
outlierinclude_mdrmd How median depend on distributional outliers from software package hctsa
pacf_features Partial autocorrelation-based features
spreadrandomlocal_meantaul Bootstrap-based stationarity measure from software package hctsa
scal_features The scaling feature set from software package hctsa
station_features The stationarity feature set from software package hctsa
trev_num Normalized nonlinear autocorrelation, the numerator of the trev function of a time series from software package hctsa
arch_stat ARCH LM Statistic
as.list.mts Convert mts object to list of time series
tsfeatures Time series feature matrix
std1st_der Standard deviation of the first derivative of the time series from software package hctsa
stl_features Strength of trend and seasonality of a time series
autocorr_features The autocorrelation feature set from software package hctsa
localsimple_taures The first zero crossing of the autocorrelation function of the residuals from Simple local time-series forecasting from software package hctsa
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Vignettes of tsfeatures

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Details

License GPL-3
LazyData true
ByteCompile true
URL https://pkg.robjhyndman.com/tsfeatures/
BugReports https://github.com/robjhyndman/tsfeatures/issues/
RoxygenNote 6.1.1
VignetteBuilder knitr
Encoding UTF-8
NeedsCompilation no
Packaged 2019-04-16 06:30:44 UTC; mitchell
Repository CRAN
Date/Publication 2019-04-16 13:02:47 UTC

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