Decompose a series by STL or MSTL

decompose_stl() splits a series into trend, seasonal pattern and remainder by LOESS (Cleveland, Cleveland, McRae & Terpenning, 1990); its defaults are those of stats::stl() and, without robust, so are its numbers (with robust = TRUE the two agree up to the eleventh robustness round and drift apart in the third digit after that). decompose_mstl() applies STL in turn to each of several seasonal periods (Bandara, Hyndman & Bergmeir, 2021).

Usage

decompose_stl(
  y,
  period = NULL,
  seasonal_window = NULL,
  trend_window = NULL,
  low_pass_window = NULL,
  degrees = NULL,
  robust = FALSE,
  inner = NULL,
  outer = NULL
)

decompose_mstl(
  y,
  periods = NULL,
  windows = NULL,
  iterations = NULL,
  robust = FALSE
)

Arguments

y

A ts or a numeric vector, longer than two full cycles.

period

The seasonal period, when y is a plain vector.

seasonal_window

The LOESS window over the cycles, an odd number, usually 7 or more (an even number is taken as the next odd one): the smaller, the faster the pattern may change. NULL keeps the same pattern in every cycle (s.window = "periodic").

trend_window, low_pass_window

LOESS windows of the trend and of the low-pass filter.

degrees

LOESS degrees (0 or 1) of the seasonal, trend and low-pass smoothers; default c(0, 1, 1).

robust

Down-weight outliers.

inner, outer

Passes of the inner loop and robustness rounds.

periods

Seasonal periods.

windows

Seasonal windows, one per period in increasing order of period (default 11, 15, 19, ...).

iterations

Rounds over the periods (default 2).

Value

An object of class foresight_decomposition, a list with trend, seasonal (a matrix, one column per period), remainder, seasonally_adjusted, periods, trend_strength and seasonal_strength (from 0 to 1; Wang, Smith & Hyndman, 2006).

Examples

d <- decompose_stl(log(AirPassengers), seasonal_window = 13)
d$seasonal_strength
#> seasonal_12 
#>    0.961261 
plot(d)