Measures the root mean squared error of the forecast scaled by the in-sample mean squared error of the naive (or seasonal naive) forecast. Values less than one indicate the forecast is better than the naive baseline.
Details
$$
\mathrm{RMSSE} = \sqrt{\frac{1}{n} \sum_{i=1}^n
\frac{(y_i - \hat y_i)^2}
{\frac{1}{T-m} \sum_{t=m+1}^T (z_t - z_{t-m})^2}}
$$
where \(z\) is the training series, \(m\) is the seasonal period, and \(T\) is the length of the
training series.
If period is NULL (default), the seasonal period is derived from task$freq and rounded to the nearest
positive integer, falling back to one when the task frequency is unavailable.
Dictionary
This mlr3::Measure can be instantiated via the dictionary mlr3::mlr_measures or with the associated sugar function mlr3::msr():
Task type
Forecast measures are registered with task_type = "regr" so they compose with the standard regression
measures (e.g. mlr3::mlr_measures_regr.rmse) on the PredictionFcst that forecast learners produce.
List them via the key prefix, not the task type, as the latter returns nothing:
Meta Information
Task type: “regr”
Range: \([0, \infty)\)
Minimize: TRUE
Average: macro
Required Prediction: “response”
Required Packages: mlr3, mlr3forecast
References
Hyndman RJ, Koehler AB (2006). “Another look at measures of forecast accuracy.” International Journal of Forecasting, 22(4), 679–688.
See also
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-eval
Package mlr3measures for the scoring functions.
as.data.table(mlr_measures)for a table of available Measures in the running session (depending on the loaded packages).Extension packages for additional task types:
mlr3proba for probabilistic supervised regression and survival analysis.
mlr3cluster for unsupervised clustering.
Other Measure:
mlr_measures_fcst.acf1,
mlr_measures_fcst.coverage,
mlr_measures_fcst.mase,
mlr_measures_fcst.mda,
mlr_measures_fcst.mdpv,
mlr_measures_fcst.mdv,
mlr_measures_fcst.mpe,
mlr_measures_fcst.msis,
mlr_measures_fcst.pinball,
mlr_measures_fcst.wape,
mlr_measures_fcst.winkler
Super classes
mlr3::Measure -> mlr3::MeasureRegr -> MeasureRMSSE