| stl {stats} | R Documentation |
Decompose a time series into seasonal, trend and irregular components
using loess, acronym STL.
stl(x, s.window, s.degree = 0,
t.window = NULL, t.degree = 1,
l.window = nextodd(period), l.degree = t.degree,
s.jump = ceiling(s.window/10),
t.jump = ceiling(t.window/10),
l.jump = ceiling(l.window/10),
robust = FALSE,
inner = if(robust) 1 else 2,
outer = if(robust) 15 else 0,
na.action = na.fail)
x |
univariate time series to be decomposed.
This should be an object of class |
s.window |
either the character string |
s.degree |
degree of locally-fitted polynomial in seasonal extraction. Should be zero or one. |
t.window |
the span (in lags) of the loess window for trend
extraction, which should be odd. If |
t.degree |
degree of locally-fitted polynomial in trend extraction. Should be zero or one. |
l.window |
the span (in lags) of the loess window of the low-pass
filter used for each subseries. Defaults to the smallest odd
integer greater than or equal to |
l.degree |
degree of locally-fitted polynomial for the subseries low-pass filter. Must be 0 or 1. |
s.jump, t.jump, l.jump |
integers at least one to increase speed of
the respective smoother. Linear interpolation happens between every
|
robust |
logical indicating if robust fitting be used in the
|
inner |
integer; the number of ‘inner’ (backfitting) iterations; usually very few (2) iterations suffice. |
outer |
integer; the number of ‘outer’ robustness iterations. |
na.action |
action on missing values. |
The seasonal component is found by loess smoothing the
seasonal sub-series (the series of all January values, ...); if
s.window = "periodic" smoothing is effectively replaced by
taking the mean. The seasonal values are removed, and the remainder
smoothed to find the trend. The overall level is removed from the
seasonal component and added to the trend component. This process is
iterated a few times. The remainder component is the
residuals from the seasonal plus trend fit.
Several methods for the resulting class "stl" objects, see,
plot.stl.
stl returns an object of class "stl" with components
time.series |
a multiple time series with columns
|
weights |
the final robust weights (all one if fitting is not done robustly). |
call |
the matched call. |
win |
integer (length 3 vector) with the spans used for the |
deg |
integer (length 3) vector with the polynomial degrees for these smoothers. |
jump |
integer (length 3) vector with the ‘jumps’ (skips) used for these smoothers. |
ni |
number of inner iterations |
no |
number of outer robustness iterations |
This is similar to but not identical to the stl function in
S-PLUS. The remainder component given by S-PLUS is the sum of
the trend and remainder series from this function.
B.D. Ripley; Fortran code by Cleveland et al (1990) from ‘netlib’.
R. B. Cleveland, W. S. Cleveland, J.E. McRae, and I. Terpenning (1990) STL: A Seasonal-Trend Decomposition Procedure Based on Loess. Journal of Official Statistics, 6, 3–73.
plot.stl for stl methods;
loess in package stats (which is not actually
used in stl).
StructTS for different kind of decomposition.
require(graphics)
plot(stl(nottem, "per"))
plot(stl(nottem, s.window = 7, t.window = 50, t.jump = 1))
plot(stllc <- stl(log(co2), s.window = 21))
summary(stllc)
## linear trend, strict period.
plot(stl(log(co2), s.window = "per", t.window = 1000))
## Two STL plotted side by side :
stmd <- stl(mdeaths, s.window = "per") # non-robust
summary(stmR <- stl(mdeaths, s.window = "per", robust = TRUE))
op <- par(mar = c(0, 4, 0, 3), oma = c(5, 0, 4, 0), mfcol = c(4, 2))
plot(stmd, set.pars = NULL, labels = NULL,
main = "stl(mdeaths, s.w = \"per\", robust = FALSE / TRUE )")
plot(stmR, set.pars = NULL)
# mark the 'outliers' :
(iO <- which(stmR $ weights < 1e-8)) # 10 were considered outliers
sts <- stmR$time.series
points(time(sts)[iO], 0.8* sts[,"remainder"][iO], pch = 4, col = "red")
par(op) # reset