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Returns range of summary measures of the forecast accuracy. Except MAAPE, all measures are defined and discussed in Hyndman and Koehler (2006).
Accuracy(f,x)Matrix giving forecast accuracy measures.
A time series forecasting object generated by iForecast.
Actual values of the same length as the time series object of f.
Ho Tsung-wu <tsungwu@ntnu.edu.tw>, College of Management, National Taiwan Normal University.
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).
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>.
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])
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