Description Usage Arguments Details Value Author(s) References See Also Examples
Methods for forecasting hierarchical or grouped time series.
1 2 3 4 5 6 7 8 9  ## S3 method for class 'gts'
forecast(object, h = ifelse(frequency(object$bts) > 1L, 2L *
frequency(object$bts), 10L), method = c("comb", "bu", "mo", "tdgsa",
"tdgsf", "tdfp"), weights = c("wls", "ols", "mint", "nseries"),
fmethod = c("ets", "arima", "rw"), algorithms = c("lu", "cg", "chol",
"recursive", "slm"), covariance = c("shr", "sam"), keep.fitted = FALSE,
keep.resid = FALSE, positive = FALSE, lambda = NULL, level,
parallel = FALSE, num.cores = 2, FUN = NULL, xreg = NULL,
newxreg = NULL, ...)

object 
Hierarchical or grouped time series object of class

h 
Forecast horizon 
method 
Method for distributing forecasts within the hierarchy. See details 
weights 
Weights used for "optimal combination" method:

fmethod 
Forecasting method to use for each series. 
algorithms 
An algorithm to be used for computing the combination
forecasts (when 
covariance 
Type of the covariance matrix to be used with

keep.fitted 
If TRUE, keep fitted values at the bottom level. 
keep.resid 
If TRUE, keep residuals at the bottom level. 
positive 
If TRUE, forecasts are forced to be strictly positive (by
setting 
lambda 
BoxCox transformation parameter. 
level 
Level used for "middleout" method (only used when 
parallel 
If TRUE, import 
num.cores 
If parallel = TRUE, specify how many cores are going to be used. 
FUN 
A userdefined function that returns an object which can be
passed to the 
xreg 
When 
newxreg 
When 
... 
Other arguments passed to 
Base methods implemented include ETS, ARIMA and the naive (random walk) models. Forecasts are distributed in the hierarchy using bottomup, topdown, middleout and optimal combination methods.
Three topdown methods are available: the two GrossSohl methods and the
forecastproportion approach of Hyndman, Ahmed, and Athanasopoulos (2011).
The "middleout" method "mo"
uses bottomup ("bu"
) for levels
higher than level
and topdown forecast proportions ("tdfp"
)
for levels lower than level
.
For nonhierarchical grouped data, only bottomup and combination methods are possible, as any method involving topdown disaggregation requires a hierarchical ordering of groups.
When xreg
and newxreg
are passed, the same covariates are
applied to every series in the hierarchy.
A forecasted hierarchical/grouped time series of class gts
.
Earo Wang, Rob J Hyndman and Shanika L Wickramasuriya
G. Athanasopoulos, R. A. Ahmed and R. J. Hyndman (2009) Hierarchical forecasts for Australian domestic tourism, International Journal of Forecasting, 25, 146166.
R. J. Hyndman, R. A. Ahmed, G. Athanasopoulos and H.L. Shang (2011) Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis, 55(9), 2579–2589. http://robjhyndman.com/papers/hierarchical/
Hyndman, R. J., Lee, A., & Wang, E. (2015). Fast computation of reconciled forecasts for hierarchical and grouped time series. Computational Statistics and Data Analysis, 97, 16–32. http://robjhyndman.com/papers/hgts/
Wickramasuriya, S. L., Athanasopoulos, G., & Hyndman, R. J. (2015). Forecasting hierarchical and grouped time series through trace minimization. Working paper 15/15, Department of Econometrics & Business Statistics, Monash University. http://robjhyndman.com/workingpapers/mint/
Gross, C. and Sohl, J. (1990) Dissagregation methods to expedite product line forecasting, Journal of Forecasting, 9, 233254.
hts
, gts
,
plot.gts
, accuracy.gts
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