View source: R/HoltWintersNew.R
ses | R Documentation |
Returns forecasts and other information for exponential smoothing forecasts
applied to y
.
ses(
y,
h = 10,
level = c(80, 95),
fan = FALSE,
initial = c("optimal", "simple"),
alpha = NULL,
lambda = NULL,
biasadj = FALSE,
x = y,
...
)
holt(
y,
h = 10,
damped = FALSE,
level = c(80, 95),
fan = FALSE,
initial = c("optimal", "simple"),
exponential = FALSE,
alpha = NULL,
beta = NULL,
phi = NULL,
lambda = NULL,
biasadj = FALSE,
x = y,
...
)
hw(
y,
h = 2 * frequency(x),
seasonal = c("additive", "multiplicative"),
damped = FALSE,
level = c(80, 95),
fan = FALSE,
initial = c("optimal", "simple"),
exponential = FALSE,
alpha = NULL,
beta = NULL,
gamma = NULL,
phi = NULL,
lambda = NULL,
biasadj = FALSE,
x = y,
...
)
y |
a numeric vector or time series of class |
h |
Number of periods for forecasting. |
level |
Confidence level for prediction intervals. |
fan |
If TRUE, level is set to seq(51,99,by=3). This is suitable for fan plots. |
initial |
Method used for selecting initial state values. If
|
alpha |
Value of smoothing parameter for the level. If |
lambda |
Box-Cox transformation parameter. If |
biasadj |
Use adjusted back-transformed mean for Box-Cox transformations. If transformed data is used to produce forecasts and fitted values, a regular back transformation will result in median forecasts. If biasadj is TRUE, an adjustment will be made to produce mean forecasts and fitted values. |
x |
Deprecated. Included for backwards compatibility. |
... |
Other arguments passed to |
damped |
If TRUE, use a damped trend. |
exponential |
If TRUE, an exponential trend is fitted. Otherwise, the trend is (locally) linear. |
beta |
Value of smoothing parameter for the trend. If |
phi |
Value of damping parameter if |
seasonal |
Type of seasonality in |
gamma |
Value of smoothing parameter for the seasonal component. If
|
ses, holt and hw are simply convenient wrapper functions for
forecast(ets(...))
.
An object of class "forecast
".
The function summary
is used to obtain and print a summary of the
results, while the function plot
produces a plot of the forecasts and
prediction intervals.
The generic accessor functions fitted.values
and residuals
extract useful features of the value returned by ets
and associated
functions.
An object of class "forecast"
is a list containing at least the
following elements:
model |
A list containing information about the fitted model |
method |
The name of the forecasting method as a character string |
mean |
Point forecasts as a time series |
lower |
Lower limits for prediction intervals |
upper |
Upper limits for prediction intervals |
level |
The confidence values associated with the prediction intervals |
x |
The original time series
(either |
residuals |
Residuals from the fitted model. |
fitted |
Fitted values (one-step forecasts) |
Rob J Hyndman
Hyndman, R.J., Koehler, A.B., Ord, J.K., Snyder, R.D. (2008) Forecasting with exponential smoothing: the state space approach, Springer-Verlag: New York. http://www.exponentialsmoothing.net.
Hyndman and Athanasopoulos (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. https://otexts.com/fpp2/
ets
, HoltWinters
,
rwf
, arima
.
fcast <- holt(airmiles)
plot(fcast)
deaths.fcast <- hw(USAccDeaths,h=48)
plot(deaths.fcast)
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