R/ts_examples.R
ts_examples.Rd
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_na_interpolation
imputes
missing values.
ts_prcomp(x, ...) ts_dygraphs(x, ...) ts_forecast(x, ...) ts_seas(x, ...) ts_na_interpolation(x, ...)
x | ts-boxable time series, an object of class |
---|---|
... | 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.
Vignette on how to make arbitrary functions ts-boxable.
# \donttest{ 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" )#>#> #>ts_plot( `Raw series` = AirPassengers, `Adjusted series` = ts_seas(AirPassengers), title = "Airline passengers", subtitle = "X-13 seasonal adjustment" )# See ?imputeTS::na_interpolation for options dta <- ts_c(mdeaths, fdeaths) dta[c(1, 3, 10), c(1, 2)] <- NA head(ts_na_interpolation(dta, option = "spline"))#> "imputeTS"#> Error: Additional packages needed. To install, use: #> #> install.packages("imputeTS")