forecast.gts: Forecast a hierarchical or grouped time series

Description

Methods for forecasting hierarchical or grouped time series.

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}

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

Value

A forecasted hierarchical/grouped time series of class gts.

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.

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.

Examples

Run this code

forecast(htseg1, h = 10, method = "bu", fmethod = "arima")

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