R package for the calculation of a set of very basic statistical properties of time-series data.
Installation
You can install the development version of basicproperties from GitHub using the following:
devtools::install_github("hendersontrent/basicproperties")Usage
basicproperties currently calculates 516 features from the following domains:
-
"distribution"—features which ignore temporal ordering and compute summary statistics of the distribution -
"ACF"—coefficients of the autocorrelation function up to a lag of 5 -
"linearity"—features associated with the linear trend of the time series -
"quantiles"—values for 100 quantiles of the data -
"fft"—the first 100 coefficients of the fast Fourier transform, including their real, imaginary, absolute, and angle components, as per thetsfreshpackage for Python
Users can compute all the features in basicproperties at once using the main function get_properties. This can be run in a one-liner as it only takes an input vector as an argument. Here is a demonstration on a vector of T = 1000 samples generated from an AR(1) process:
library(basicproperties)
y <- arima.sim(model = list(ar = 0.8), n = 1000)
outs <- get_properties(y)Here is a sample of twenty of the features:
feature_name values feature_set
1 mean -0.0682273117 distribution
2 median -0.0285242011 distribution
3 mode NA distribution
4 min -5.3285657460 distribution
5 max 4.5480362786 distribution
6 skewness -0.1532091500 distribution
7 kurtosis -0.2380394101 distribution
8 acf_1 0.8040463790 ACF
9 acf_2 0.6564698047 ACF
10 acf_3 0.5495283435 ACF
11 acf_4 0.4743650582 ACF
12 acf_5 0.3881577375 ACF
13 IQR 2.2607101884 distribution
14 sd 1.6758453734 distribution
15 linear_trend -0.0003344607 linearity
16 prop_above_3SD 0.0010000000 distribution
17 quantile_1 -4.0865978434 quantiles
18 quantile_2 -3.5270973394 quantiles
19 quantile_3 -3.3587224511 quantiles
20 quantile_4 -3.1733633912 quantiles