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Time-series decomposition plot

Source: R/timeseries.R
decompose_plot.Rd

Decomposes a seasonal time series into trend, seasonal and remainder components and shows them, with the original series, as stacked panels.

Usage

decompose_plot(
  x,
  frequency = NULL,
  method = c("stl", "classical"),
  robust = FALSE,
  confidence = FALSE,
  level = 0.95,
  title = NULL
)

Arguments

x

A ts object, or a numeric vector (then frequency is required).

frequency

Number of observations per period (e.g. 12 for monthly data); taken from x when it is a ts.

method

"stl" (loess-based) or "classical" (stats::decompose()).

robust

For method = "stl", whether to fit a robust STL (stats::stl() with robust = TRUE), which down-weights outliers in the loess fits so that an unusual point bleeds less into the trend and seasonal components. Ignored for the classical method.

confidence

Whether to draw a confidence ribbon around the trend component. The band is a normal-approximation interval based on the remainder's standard deviation (see Details). FALSE reproduces the previous behaviour exactly.

level

Coverage of the trend confidence ribbon (a single number strictly between 0 and 1).

title

Plot title.

Value

A 'patchwork' object (printable like a ggplot2::ggplot).

Details

The trend confidence ribbon is a pragmatic normal-approximation band: trend +/- z * sd(remainder), where z = qnorm((1 + level) / 2) and the remainder standard deviation is computed on the non-missing remainder values. It conveys the scale of the unexplained variation around the smoothed trend rather than a formal sampling-distribution interval.

See also

ts_forecast(), seasonal_plot()

Examples

decompose_plot(AirPassengers)

# \donttest{
decompose_plot(AirPassengers, method = "classical")

# Robust STL with a confidence ribbon on the trend
decompose_plot(AirPassengers, robust = TRUE, confidence = TRUE)

# }