accuracy.gts

0th

Percentile

In-sample or out-of-sample accuracy measures for forecast grouped and hierarchical model

Returns a range of summary measures of the forecast accuracy. The function measures out-of-sample forecast accuracy based on (holdout data - forecasts) and in-sample accuracy at the bottom level when setting keep.fitted = TRUE in the forecast.gts. All measures are defined and discussed in Hyndman and Koehler (2006).

Keywords
error
Usage
# S3 method for gts
accuracy(f, test, levels, ...)
Arguments
f

An object of class gts, containing the forecasted hierarchical or grouped time series. In-sample accuracy at the bottom level returns when test is missing.

test

An object of class gts, containing the holdout hierarchical time series

levels

Return the specified level(s), when carrying out out-of-sample

...

Extra arguments to be ignored

Details

MASE calculation is scaled using MAE of in-sample naive forecasts for non-seasonal time series, and in-sample seasonal naive forecasts for seasonal time series.

Value

Matrix giving forecast accuracy measures.

ME

Mean Error

RMSE

Root Mean Square Error

MAE

Mean Absolute Error

MAPE

Mean Absolute Percentage Error

MPE

Mean Percentage Error

MASE

Mean Absolute Scaled Error

References

R. J. Hyndman and A. Koehler (2006), Another look at measures of forecast accuracy, International Journal of Forecasting, 22, 679-688.

See Also

hts, plot.gts, forecast.gts, accuracy

Aliases
  • accuracy.gts
Examples
# NOT RUN {
data <- window(htseg2, start = 1992, end = 2002)
test <- window(htseg2, start = 2003)
fcasts <- forecast(data, h = 5, method = "bu")
accuracy(fcasts, test)
accuracy(fcasts, test, levels = 1)

# }
Documentation reproduced from package hts, version 5.1.5, License: GPL (>= 2)

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