| ts_examples | R Documentation |
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. |
With the exception of ts_prcomp, these functions depend on external
packages.
a ts-boxable object of the same class as x, i.e., an object of
class ts, xts, zoo, zooreg, data.frame, data.table, tbl,
tbl_ts, tbl_time, tis, irts or timeSeries.
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"
)
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"))
ts_dygraphs(ts_c(mdeaths, EuStockMarkets))
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