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 (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).

Keywords
ts
Usage
accuracy(f, x, test=NULL, d=NULL, D=NULL)
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, or a time series overlapping with the times of f.
test
Indicator of which elements of x and f to test. If test is NULL, all elements are used. Otherwise test is a numeric vector containing the indices of the elements to use in the test.
d
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.
D
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.
Details

The measures calculated are:

  • ME: Mean Error
  • RMSE: Root Mean Squared Error
  • MAE: Mean Absolute Error
  • MPE: Mean Percentage Error
  • MAPE: Mean Absolute Percentage Error
  • MASE: Mean Absolute Scaled Error
  • ACF1: Autocorrelation of errors at lag 1.
By default, the 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.

See Hyndman and Koehler (2006) and Hyndman and Athanasopoulos (2014, Section 2.5) for further details.

Value

  • Matrix 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), 679-688. Hyndman, R.J. and Athanasopoulos, G. (2014) "Forecasting: principles and practice", OTexts. Section 2.5 "Evaluating forecast accuracy". http://www.otexts.org/fpp/2/5.

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 6.0, License: GPL (>= 2)

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