rmsse | R Documentation |
A pretty fast way to compute commong accuracy measures
rmsse(resid_sqr, scale, na.rm = TRUE)
mase(resid, scale, na.rm = TRUE)
mpe(scl_prc_resid, na.rm = TRUE)
mape(scl_prc_resid, na.rm = TRUE)
maape(scl_prc_resid, na.rm = TRUE)
me(resid, na.rm = TRUE)
rmse(resid_sqr, na.rm = TRUE)
mae(resid, na.rm = TRUE)
acf1(resid, na.rm = TRUE, demean = TRUE)
fast_measure(resid, actual, scale = 1, na.rm = TRUE)
fast_accuracy(
fable,
test,
train = NULL,
measures = fast_measure,
across = c(".model", "h"),
...
)
resid_sqr |
Squared residuals |
scale |
Scaling factor - typically mean of training data |
na.rm |
Whether to remove NA values - defaults to **TRUE** |
resid |
Residuals |
scl_prc_resid |
Scaled percentage residuals |
demean |
Whether to demean before calculating autocorrelations - defaults to **TRUE** |
actual |
Actuals |
fable |
The model fable - we do not work with mables |
test |
The test data corresponding to forecasts in the fable |
train |
The original data used in fitting the models - if available, scaled accuracy measures will be computed by default. |
measures |
Currently only for future extensions, though **fast_measure** does everything that was required so far |
across |
The grouping variables across which to compute measures - by default just the ".model" column, but you can easily pass something like "h" and it will work. |
... |
For future extensions :) |
A data.frame with computed accuracy measures, grouped by whatever was supplied in **across** and the keys present in **test**.
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