Accuracy measures for forecast model
Returns range of summary measures of the forecast accuracy. If
x is provided, the function measures out-of-sample (test set) forecast accuracy
based on x-f. If
x is not provided, the function only produces in-sample (training set) accuracy measures of the forecasts based on f["x"]-fitted(f).
All measures are defined and discussed in Hyndman and Koehler (2006).
accuracy(f, x, test=NULL, d=NULL, D=NULL)
- An object of class
"forecast", or a numerical vector containing forecasts. It will also work with
xis omitted -- in which case in-sample accuracy measures are r
- An optional numerical vector containing actual values of the same length as object, or a time series overlapping with the times of
- Indicator of which elements of x and f to test. If
NULL, all elements are used. Otherwise test is a numeric vector containing the indices of the elements to use in the test.
- An integer indicating the number of lag-1 differences to be used for the denominator in MASE calculation. Default value is 1 for non-seasonal series and 0 for seasonal series.
- An integer indicating the number of seasonal differences to be used for the denominator in MASE calculation. Default value is 0 for non-seasonal series and 1 for seasonal series.
By default, 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.
- Matrix giving forecast accuracy measures.
Hyndman, R.J. and Koehler, A.B. (2006) "Another look at measures of forecast accuracy". International Journal of Forecasting, 22(4).
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])