Time series feature matrix

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,
  ...
)

Arguments

tslist

a list of univariate time series, each of class ts or a numeric vector. Alternatively, an object of class mts may be used.

features

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.

scale

if TRUE, time series are scaled to mean 0 and sd 1 before features are computed.

trim

if TRUE, time series are trimmed by trim_amount before features are computed. Values larger than trim_amount in absolute value are set to NA.

trim_amount

Default level of trimming if trim==TRUE.

parallel

If TRUE, multiple cores (or multiple sessions) will be used. This only speeds things up when there are a large number of time series.

multiprocess

The function from the future package to use for parallel processing. Either multisession or multicore. The latter is preferred for Linux and MacOS.

na.action

A function to handle missing values. Use na.interp to estimate missing values.

...

Other arguments get passed to the feature functions.

Value

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.

Author

Rob J Hyndman

Examples

mylist <- list(sunspot.year, WWWusage, AirPassengers, USAccDeaths)
tsfeatures(mylist)
#> # A tibble: 4 × 20
#>   frequency nperi…¹ seaso…² trend   spike linea…³ curva…⁴ e_acf1 e_acf10 entropy
#>       <dbl>   <dbl>   <dbl> <dbl>   <dbl>   <dbl>   <dbl>  <dbl>   <dbl>   <dbl>
#> 1         1       0       1 0.125 2.10e-5    3.58    1.11  0.793   2.21    0.702
#> 2         1       0       1 0.985 3.01e-8    4.45    1.10  0.774   0.983   0.461
#> 3        12       1      12 0.991 1.46e-8   11.0     1.09  0.509   0.930   0.296
#> 4        12       1      12 0.802 9.15e-7   -2.12    2.85  0.258   0.341   0.548
#> # … with 10 more variables: 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>, and
#> #   abbreviated variable names ¹​nperiods, ²​seasonal_period, ³​linearity,
#> #   ⁴​curvature