Example Functions, Generated by ts_. ts_prcomp calculates the principal components of multiple time series, ts_dygraphs generates an interactive graphical visualization, ts_forecast return an univariate forecast, ts_seas the seasonally adjusted series.

ts_prcomp(x, ...)

ts_dygraphs(x, ...)

ts_forecast(x, ...)

ts_seas(x, ...)



ts-boxable time series, an object of class ts, xts, zoo, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.


further arguments, passed to the underlying function. For help, consider these functions, e.g., stats::prcomp.


Usually, a ts-boxable time series, with the same class as the input. ts_dygraphs draws a plot.


With the exception of ts_prcomp, these functions depend on external packages.

See also

Vignette on how to make arbitrary functions ts-boxable.


ts_plot( ts_scale(ts_c( Male = mdeaths, Female = fdeaths, `First principal compenent` = -ts_prcomp(ts_c(mdeaths, fdeaths))[, 1] )), title = "Deaths from lung diseases", subtitle = "Normalized values" )
ts_plot(ts_c( male = mdeaths, female = fdeaths, ts_forecast(ts_c(`male (fct)` = mdeaths, `female (fct)` = fdeaths))), title = "Deaths from lung diseases", subtitle = "Exponential smoothing forecast" )
#> Registered S3 method overwritten by 'quantmod': #> method from #> as.zoo.data.frame zoo
#> Registered S3 methods overwritten by 'forecast': #> method from #> fitted.fracdiff fracdiff #> residuals.fracdiff fracdiff
ts_plot( `Raw series` = AirPassengers, `Adjusted series` = ts_seas(AirPassengers), title = "Airline passengers", subtitle = "X-13 seasonal adjustment" )
ts_dygraphs(ts_c(mdeaths, EuStockMarkets))