R: Accuracy measures for a forecast model

accuracy {forecast} | R Documentation

## Accuracy measures for a forecast model

### Description

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

### Usage

    accuracy(f, ...)

    ## Default S3 method:
    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 training set accuracy measures are returned.
`...` | Additional arguments depending on the specific method.
`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 training set naive forecasts for non-seasonal time series, training set seasonal naive forecasts for seasonal time series and training set mean forecasts for non-time series data. If `f` is a numerical vector rather than a `forecast` object, the MASE will not be returned as the training data will not be available.

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

### Value

Matrix giving forecast accuracy measures.

### Author(s)

Rob J Hyndman

### 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. (2018) "Forecasting: principles and practice", 2nd ed., OTexts, Melbourne, Australia. Section 3.4 "Evaluating forecast accuracy". https://otexts.org/fpp2/accuracy.html.

### 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])

[Package forecast version 8.4 Index]