Description Usage Arguments Details Value Author(s) References See Also Examples
Function selects the best State Space ARIMA based on information criteria, using fancy branch and bound mechanism. The resulting model can be not optimal in IC meaning, but it is usually reasonable.
1 2 3 4 5 6 7 8 9  auto.ssarima(y, orders = list(ar = c(3, 3), i = c(2, 1), ma = c(3, 3)),
lags = c(1, frequency(y)), combine = FALSE, fast = TRUE,
constant = NULL, initial = c("backcasting", "optimal"), ic = c("AICc",
"AIC", "BIC", "BICc"), loss = c("likelihood", "MSE", "MAE", "HAM", "MSEh",
"TMSE", "GTMSE", "MSCE"), h = 10, holdout = FALSE, cumulative = FALSE,
interval = c("none", "parametric", "likelihood", "semiparametric",
"nonparametric"), level = 0.95, bounds = c("admissible", "none"),
silent = c("all", "graph", "legend", "output", "none"), xreg = NULL,
xregDo = c("use", "select"), initialX = NULL, ...)

y 
Vector or ts object, containing data needed to be forecasted. 
orders 
List of maximum orders to check, containing vector variables

lags 
Defines lags for the corresponding orders (see examples). The
length of 
combine 
If 
fast 
If 
constant 
If 
initial 
Can be either character or a vector of initial states. If it
is character, then it can be 
ic 
The information criterion used in the model selection procedure. 
loss 
The type of Loss Function used in optimization. There are also available analytical approximations for multistep functions:
Finally, just for fun the absolute and half analogues of multistep estimators
are available: 
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. 
bounds 
What type of bounds to use in the model estimation. The first letter can be used instead of the whole word. 
silent 
If 
xreg 
The vector (either numeric or time series) or the matrix (or
data.frame) of exogenous variables that should be included in the model. If
matrix included than columns should contain variables and rows  observations.
Note that 
xregDo 
The variable defines what to do with the provided xreg:

initialX 
The vector of initial parameters for exogenous variables.
Ignored if 
... 
Other nondocumented parameters. For example 
The function constructs bunch of ARIMAs in Single Source of Error state space form (see ssarima documentation) and selects the best one based on information criterion. The mechanism is described in Svetunkov & Boylan (2019).
Due to the flexibility of the model, multiple seasonalities can be used. For example, something crazy like this can be constructed: SARIMA(1,1,1)(0,1,1)[24](2,0,1)[24*7](0,0,1)[24*30], but the estimation may take a lot of time... It is recommended to use auto.msarima in cases with more than one seasonality and high frequencies.
For some more information about the model and its implementation, see the
vignette: vignette("ssarima","smooth")
Object of class "smooth" is returned. See ssarima for details.
Ivan Svetunkov, ivan@svetunkov.ru
Snyder, R. D., 1985. Recursive Estimation of Dynamic Linear Models. Journal of the Royal Statistical Society, Series B (Methodological) 47 (2), 272276.
Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D. (2008) Forecasting with exponential smoothing: the state space approach, SpringerVerlag. doi: 10.1007/9783540719182.
Svetunkov Ivan and Boylan John E. (2017). Multiplicative StateSpace Models for Intermittent Time Series. Working Paper of Department of Management Science, Lancaster University, 2017:4 , 143.
Teunter R., Syntetos A., Babai Z. (2011). Intermittent demand: Linking forecasting to inventory obsolescence. European Journal of Operational Research, 214, 606615.
Croston, J. (1972) Forecasting and stock control for intermittent demands. Operational Research Quarterly, 23(3), 289303.
Syntetos, A., Boylan J. (2005) The accuracy of intermittent demand estimates. International Journal of Forecasting, 21(2), 303314.
Svetunkov, I., & Boylan, J. E. (2019). Statespace ARIMA for supplychain forecasting. International Journal of Production Research, 0(0), 1–10. doi: 10.1080/00207543.2019.1600764
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  x < rnorm(118,100,3)
# The best ARIMA for the data
ourModel < auto.ssarima(x,orders=list(ar=c(2,1),i=c(1,1),ma=c(2,1)),lags=c(1,12),
h=18,holdout=TRUE,interval="np")
# The other one using optimised states
auto.ssarima(x,orders=list(ar=c(3,2),i=c(2,1),ma=c(3,2)),lags=c(1,12),
initial="o",h=18,holdout=TRUE)
# And now combined ARIMA
auto.ssarima(x,orders=list(ar=c(3,2),i=c(2,1),ma=c(3,2)),lags=c(1,12),
combine=TRUE,h=18,holdout=TRUE)
summary(ourModel)
forecast(ourModel)
plot(forecast(ourModel))

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