forecast.gts

0th

Percentile

Forecast a hierarchical or grouped time series

Methods for forecasting hierarchical or grouped time series.

Keywords
ts
Usage
## 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, ...)
Arguments
object
Hierarchical or grouped time series object of class {gts}
h
Forecast horizon
method
Method for distributing forecasts within the hierarchy. See details
fmethod
Forecasting method to use
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
lambda
Box-Cox transformation parameter
weights
Weights used for "optimal combination" method. When 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.
level
Level used for "middle-out" method (only used when method="mo")
parallel
If TRUE, import parallel package to allow parallel processing
num.cores
If parallel = TRUE, specify how many cores are going to be used
xreg
When fmethod = "arima", a vector or matrix of external regressors, which must have the same number of rows as the original univariate time series
newxreg
When fmethod = "arima", a vector or matrix of external regressors, which must have the same number of rows as the original univariate time series
...
Other arguments passing to ets or auto.arima
Details

Base methods implemented include ETS, ARIMA and the naive (random walk) models. Forecasts are distributed in the hierarchy using bottom-up, top-down, middle-out and optimal combination methods. Three top-down methods are available: the two Gross-Sohl methods and the forecast-proportion approach of Hyndman, Ahmed, and Athanasopoulos (2011). The "middle-out" method "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.

Value

  • A forecasted hierarchical/grouped time series of class gts.

References

G. Athanasopoulos, R. A. Ahmed and R. J. Hyndman (2009) Hierarchical forecasts for Australian domestic tourism, International Journal of Forecasting, 25, 146-166.

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/

Gross, C. and Sohl, J. (1990) Dissagregation methods to expedite product line forecasting, Journal of Forecasting, 9, 233-254.

See Also

hts, gts, plot.gts, accuracy.gts

Aliases
  • forecast.gts
Examples
forecast(htseg1, h = 10, method = "bu", fmethod = "arima")
Documentation reproduced from package hts, version 4.0, License: GPL (>= 2)

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