Point estimate accuracy measures

```
ME(.resid, na.rm = TRUE, ...)
MSE(.resid, na.rm = TRUE, ...)
RMSE(.resid, na.rm = TRUE, ...)
MAE(.resid, na.rm = TRUE, ...)
MPE(.resid, .actual, na.rm = TRUE, ...)
MAPE(.resid, .actual, na.rm = TRUE, ...)
MASE(
.resid,
.train,
demean = FALSE,
na.rm = TRUE,
.period,
d = .period == 1,
D = .period > 1,
...
)
RMSSE(
.resid,
.train,
demean = FALSE,
na.rm = TRUE,
.period,
d = .period == 1,
D = .period > 1,
...
)
ACF1(.resid, na.action = stats::na.pass, demean = TRUE, ...)
point_accuracy_measures
```

An object of class `list`

of length 8.

- .resid
A vector of residuals from either the training (model accuracy) or test (forecast accuracy) data.

- na.rm
Remove the missing values before calculating the accuracy measure

- ...
Additional arguments for each measure.

- .actual
A vector of responses matching the fitted values (for forecast accuracy,

`new_data`

must be provided).- .train
A vector of responses used to train the model (for forecast accuracy, the

`orig_data`

must be provided).- demean
Should the response be demeaned (MASE)

- .period
The seasonal period of the data (defaulting to 'smallest' seasonal period). from a model, or forecasted values from the forecast.

- d
Should the response model include a first difference?

- D
Should the response model include a seasonal difference?

- na.action
Function to handle missing values.