iForecast (version 1.1.1)

Accuracy: Accuracy measures for a forecast model

Description

Returns range of summary measures of the forecast accuracy. Except MAAPE, all measures are defined and discussed in Hyndman and Koehler (2006).

Usage

Accuracy(f,x)

Value

Matrix giving forecast accuracy measures.

Arguments

f

A time series forecasting object generated by iForecast.

x

Actual values of the same length as the time series object of f.

Author

Ho Tsung-wu <[e-mail]>, College of Management, National Taiwan Normal University.

Details

The measures calculated are:

• RMSE: Root Mean Squared Error

• MAE: Mean Absolute Error

• MAPE: Mean Absolute Percentage Error

• MAAPE: Mean Absolute Arctan Percentage Error

• ACF1: Autocorrelation of errors at lag 1.

Except MAAPE, by default, see Hyndman and Koehler (2006) and Hyndman and Athanasopoulos (2014, Section 2.5) for further details. For MAAPE, please see Kim and Kim (2016).

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.com/fpp2/accuracy.html>
Kim Sungil and Heeyoung Kim (2016) "A new metric of absolute percentage error for intermittent demand forecasts", International Journal of Forecasting,32(3),669-679. <https://doi.org/10.1016/j.ijforecast.2015.12.003>.

Examples

tmp0=timeSeries::as.timeSeries(ts(rnorm(800),start=c(1960,1),freq=12))
fit1 <- timeSeries::as.timeSeries(forecast::rwf(tmp0[1:700,1],h=100)$mean)
Accuracy(f=fit1,x=tmp0[701:800,1])