## S3 method for class 'gts':
forecast(object, h = ifelse(frequency(object) > 1L,
2L * frequency(object), 10L),
method = c("comb", "bu", "mo",
"tdgsa", "tdgsf", "tdfp"),
fmethod = c("ets", "arima", "rw"),
keep.fitted = FALSE, keep.resid = FALSE,
positive = FALSE, lambda = NULL, level,
weights = c("none", "sd", "nseries"),
parallel = FALSE, num.cores = NULL,
xreg = NULL, newxreg = NULL, ...)
{gts}
weights = "sd"
,
it takes account of the standard deviation of forecasts;
when weights
= "nseries", weights are equal to the inverse of row sums of the summing matrix.method="mo"
)parallel
package to allow parallel processingfmethod = "arima"
, a vector or matrix of external regressors, which must have the same number of rows as the original univariate time seriesfmethod = "arima"
, a vector or matrix of external regressors, which must have the same number of rows as the original univariate time seriesets
or auto.arima
gts
."mo"
uses bottom-up ("bu"
) for levels higher than level
and top-down forecast proportions ("tdfp"
) for levels lower than level
. For non-hierarchical grouped data, only bottom-up and combination methods are possible, as any method involving top-down disaggregation requires a hierarchical ordering of groups.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.
Gross, C. and Sohl, J. (1990) Dissagregation methods to expedite product line forecasting, Journal of Forecasting, 9, 233-254.
hts
, gts
, plot.gts
, accuracy.gts
forecast(htseg1, h = 10, method = "bu", fmethod = "arima")
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