Description Usage Arguments Details Value Author(s) See Also Examples
Forecasts of STL objects are obtained by applying a non-seasonal forecasting method to the seasonally adjusted data and re-seasonalizing using the last year of the seasonal component.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | stlm(y, s.window=7, robust=FALSE, method=c("ets","arima"), modelfunction=NULL,
model=NULL, etsmodel="ZZN", lambda=NULL, biasadj=FALSE, xreg=NULL,
allow.multiplicative.trend=FALSE, x=y, ...)
stlf(y, h=frequency(x)*2, s.window=7, t.window=NULL, robust=FALSE,
lambda=NULL, biasadj=FALSE, x=y, ...)
## S3 method for class 'stlm'
forecast(object, h = 2*object$m,
level = c(80, 95), fan = FALSE, lambda=object$lambda, biasadj=NULL, newxreg=NULL,
allow.multiplicative.trend=FALSE, ...)
## S3 method for class 'stl'
forecast(object, method=c("ets","arima","naive","rwdrift"),
etsmodel="ZZN", forecastfunction=NULL,
h=frequency(object$time.series)*2, level=c(80,95),
fan=FALSE, lambda=NULL, biasadj=NULL, xreg=NULL, newxreg=NULL,
allow.multiplicative.trend=FALSE, ...)
|
y |
A univariate numeric time series of class |
object |
An object of class |
method |
Method to use for forecasting the seasonally adjusted series. |
modelfunction |
An alternative way of specifying the function for modelling the seasonally adjusted series. If |
model |
Output from a previous call to |
forecastfunction |
An alternative way of specifying the function for forecasting the seasonally adjusted series. If |
etsmodel |
The ets model specification passed to |
xreg |
Historical regressors to be used in |
newxreg |
Future regressors to be used in |
h |
Number of periods for forecasting. |
level |
Confidence level for prediction intervals. |
fan |
If |
lambda |
Box-Cox transformation parameter. Ignored if |
biasadj |
Use adjusted back-transformed mean for Box-Cox transformations. If TRUE, point forecasts and fitted values are mean forecast. Otherwise, these points can be considered the median of the forecast densities. |
s.window |
Either the character string “periodic” or the span (in lags) of the loess window for seasonal extraction. |
t.window |
A number to control the smoothness of the trend. See |
robust |
If |
allow.multiplicative.trend |
If TRUE, then ETS models with multiplicative trends are allowed. Otherwise, only additive or no trend ETS models are permitted. |
x |
Deprecated. Included for backwards compatibility. |
... |
Other arguments passed to |
stlm
takes a time series y
, applies an STL decomposition, and models the seasonally adjusted data using the model passed as modelfunction
or specified using method
. It returns an object that includes the original STL decomposition and a time series model fitted to the seasonally adjusted data. This object can be passed to the forecast.stlm
for forecasting.
forecast.stlm
forecasts the seasonally adjusted data, then re-seasonalizes the results by adding back the last year of the estimated seasonal component.
stlf
combines stlm
and forecast.stlm
. It takes a ts
argument, applies an STL decomposition, models the seasonally adjusted data, reseasonalizes, and returns the forecasts. However, it allows more general forecasting methods to be specified via forecastfunction
.
forecast.stl
is similar to stlf
except that it takes the STL decomposition as the first argument, instead of the time series.
Note that the prediction intervals ignore the uncertainty associated with the seasonal component. They are computed using the prediction intervals from the seasonally adjusted series, which are then reseasonalized using the last year of the seasonal component. The uncertainty in the seasonal component is ignored.
The time series model for the seasonally adjusted data can be specified in stlm
using either method
or modelfunction
. The method
argument provides a shorthand way of specifying modelfunction
for a few special cases. More generally, modelfunction
can be any function with first argument a ts
object, that returns an object that can be passed to forecast
. For example, forecastfunction=ar
uses the ar
function for modelling the seasonally adjusted series.
The forecasting method for the seasonally adjusted data can be specified in stlf
and forecast.stl
using either method
or forecastfunction
. The method
argument provides a shorthand way of specifying forecastfunction
for a few special cases. More generally, forecastfunction
can be any function
with first argument a ts
object, and other h
and level
, which returns an object of class forecast
. For example, forecastfunction=thetaf
uses the thetaf
function for forecasting the seasonally adjusted series.
stlm
returns an object of class stlm
. The other functions return objects of class forecast
.
There are many methods for working with forecast
objects including summary
to obtain and print a summary of the
results, while plot
produces a plot of the forecasts and prediction intervals.
The generic accessor functions fitted.values
and residuals
extract useful features.
Rob J Hyndman
stl
, forecast.ets
, forecast.Arima
.
1 2 3 4 5 6 7 | tsmod <- stlm(USAccDeaths, modelfunction=ar)
plot(forecast(tsmod, h=36))
plot(stlf(AirPassengers, lambda=0))
decomp <- stl(USAccDeaths,s.window="periodic")
plot(forecast(decomp))
|
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