Description Usage Arguments Details Value Author(s) References See Also Examples
Function constructs state space simple moving average of predefined order
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y 
Vector or ts object, containing data needed to be forecasted. 
order 
Order of simple moving average. If 
ic 
The information criterion used in the model selection procedure. 
h 
Length of forecasting horizon. 
holdout 
If 
cumulative 
If 
interval 
Type of interval to construct. This can be:
The parameter also accepts 
level 
Confidence level. Defines width of prediction interval. 
silent 
If 
... 
Other nondocumented parameters. For example parameter

The function constructs AR model in the Single Source of Error state space form based on the idea that:
y_{t} = \frac{1}{n} ∑_{j=1}^n y_{tj}
which is AR(n) process, that can be modelled using:
y_{t} = w' v_{t1} + ε_{t}
v_{t} = F v_{t1} + g ε_{t}
Where v_{t} is a state vector.
For some more information about the model and its implementation, see the
vignette: vignette("sma","smooth")
Object of class "smooth" is returned. It contains the list of the following values:
model
 the name of the estimated model.
timeElapsed
 time elapsed for the construction of the model.
states
 the matrix of the fuzzy components of ssarima, where
rows
correspond to time and cols
to states.
transition
 matrix F.
persistence
 the persistence vector. This is the place, where
smoothing parameters live.
measurement
 measurement vector of the model.
order
 order of moving average.
initial
 Initial state vector values.
initialType
 Type of initial values used.
nParam
 table with the number of estimated / provided parameters.
If a previous model was reused, then its initials are reused and the number of
provided parameters will take this into account.
fitted
 the fitted values.
forecast
 the point forecast.
lower
 the lower bound of prediction interval. When
interval=FALSE
then NA is returned.
upper
 the higher bound of prediction interval. When
interval=FALSE
then NA is returned.
residuals
 the residuals of the estimated model.
errors
 The matrix of 1 to h steps ahead errors.
s2
 variance of the residuals (taking degrees of freedom into
account).
interval
 type of interval asked by user.
level
 confidence level for interval.
cumulative
 whether the produced forecast was cumulative or not.
y
 the original data.
holdout
 the holdout part of the original data.
ICs
 values of information criteria of the model. Includes AIC,
AICc, BIC and BICc.
logLik
 loglikelihood of the function.
lossValue
 Cost function value.
loss
 Type of loss function used in the estimation.
accuracy
 vector of accuracy measures for the
holdout sample. Includes: MPE, MAPE, SMAPE, MASE, sMAE, RelMAE, sMSE and
Bias coefficient (based on complex numbers). This is available only when
holdout=TRUE
.
Ivan Svetunkov, ivan@svetunkov.ru
Svetunkov I. (2015  Inf) "smooth" package for R  series of posts about the underlying models and how to use them: https://forecasting.svetunkov.ru/en/tag/smooth/.
Svetunkov I. (2017). Statistical models underlying functions of 'smooth' package for R. Working Paper of Department of Management Science, Lancaster University 2017:1, 152.
Svetunkov, I., & Petropoulos, F. (2017). Old dog, new tricks: a modelling view of simple moving averages. International Journal of Production Research, 7543(January), 114. doi: 10.1080/00207543.2017.1380326
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