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 returned.
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.

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.

• 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 7.2, License: GPL (>= 2)

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