accuracy: Accuracy measures for forecast model
In pli2016/forecast: Forecasting Functions for Time Series and Linear Models

Description Usage Arguments Details Value Author(s) References Examples

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).

1	accuracy(f, x, test=NULL, d=NULL, D=NULL)

`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.

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.

Matrix giving forecast accuracy measures.

Rob J Hyndman

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.

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])