Description
Time Series Feature Extraction.
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.
README.md
tsfeatures
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 × 20
#> frequency nperiods seasonal_period trend spike linearity curvature e_acf1
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 1 0.125 2.10e-5 3.58 1.11 0.793
#> 2 1 0 1 0.985 3.01e-8 4.45 1.10 0.774
#> 3 12 1 12 0.991 1.46e-8 11.0 1.09 0.509
#> 4 12 1 12 0.802 9.15e-7 -2.12 2.85 0.258
#> # ℹ 12 more variables: 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.