accuracy

0th

Percentile

Accuracy measures for forecast model

Returns range of summary measures of the forecast accuracy. If x is provided, the function measures out-of-sample forecast accuracy based on x-f. If x is not provided, the function produces in-sample accuracy measures of the forecasts based on f["x"]-fitted(f). All measures are defined and discussed in Hyndman and Koehler (2006).

Keywords
ts
Usage
accuracy(f, x, test="all")
Arguments
f
An object of class "forecast", or a numerical vector containing forecasts. It will also work with Arima, ets and lm objects if x is omitted -- in which case in-sample accuracy measures are r
x
An optional numerical vector containing actual values of the same length as object.
test
Indicator of which elements of x and f to test. If test=="all", all elements are used. Otherwise test is a numeric vector containing the indices of the elements to use in the test.
Details

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

Value

  • Vector giving forecast accuracy measures.

References

Hyndman, R.J. and Koehler, A.B. (2006) "Another look at measures of forecast accuracy". International Journal of Forecasting, 22(4).

Aliases
  • accuracy
Examples
fit1 <- rwf(EuStockMarkets[1:200,1],h=100)
fit2 <- meanf(EuStockMarkets[1:200,1],h=100)
accuracy(fit1)
accuracy(fit2)
accuracy(fit1,EuStockMarkets[201:300,1])
accuracy(fit2,EuStockMarkets[201:300,1])
plot(fit1)
lines(EuStockMarkets[1:300,1])
Documentation reproduced from package forecast, version 3.21, License: GPL (>= 2)

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