Time series feature matrixSource:
tsfeatures computes a matrix of time series features from a list of time series
a list of univariate time series, each of class
tsor a numeric vector. Alternatively, an object of class
mtsmay be used.
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.
TRUE, time series are scaled to mean 0 and sd 1 before features are computed.
TRUE, time series are trimmed by
trim_amountbefore features are computed. Values larger than
trim_amountin absolute value are set to
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
na.interpto estimate missing values.
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.
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 #> # 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>