tsfeatures computes a matrix of time series features from a list of time series
The tsfeature package provides methods to extract various features from time series data
tsfeatures( tslist, features = c("frequency", "stl_features", "entropy", "acf_features"), scale = TRUE, trim = FALSE, trim_amount = 0.1, parallel = FALSE, multiprocess = future::multisession, na.action = na.pass, ... )
a list of univariate time series, each of class
a vector of function names which return numeric vectors of features. All features returned by these functions must be named if they return more than one feature. Existing functions from installed packages may be used, but the package must be loaded first. Functions must return a result for all time series, even if it is just NA.
Default level of trimming if
If TRUE, multiple cores (or multiple sessions) will be used. This only speeds things up when there are a large number of time series.
A function to handle missing values. Use
Other arguments get passed to the feature functions.
A feature matrix (in the form of a tibble) with each row corresponding to one time series from tslist, and each column being a feature.
Rob J Hyndman
mylist <- list(sunspot.year, WWWusage, AirPassengers, USAccDeaths) tsfeatures(mylist)#> # 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 #> # … with 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>