mlr3forecast 0.2.0
- BREAKING CHANGE: Key columns are no longer features by default.
Restore the feature role explicitly when needed.
- BREAKING CHANGE:
po("fcstavg") was renamed to
po("fcst.avg") to match the id prefix of the other
PipeOps.
- feat:
DirectForecaster gained
$importance(), $selected_features(), and
$oob_error() methods returning one result per horizon
model, named like $native_model.
- feat: New learner
fcst.ar wrapping
stats::ar(), fitting autoregressive models by Yule-Walker,
Burg, OLS, or maximum likelihood with optional AIC order selection.
- feat:
fcst.mean gained the bootstrap and
npaths parameters for empirical quantiles resampled from
the residuals.
- feat: New learner
fcst.sparma wrapping
smooth::sparma(), fitting sparse ARMA models whose
orders map to specific lags instead of expanding
polynomials.
- feat:
forecast() now validates newdata as
a data frame with unique column names.
- feat:
partition() now validates ratio
before partitioning a TaskFcst.
- feat:
mlr3::set_threads() support: the
num.cores parameter of fcst.arfima,
fcst.auto_arima, fcst.nnetar,
fcst.bats, and fcst.tbats now carries the
"threads" tag, and setting num.cores to a
value greater than one enables the corresponding parallel switch at
train time.
- fix:
fcst.nnetar now declares nnet in its
packages so parallel training finds predict.nnet on the
main process.
- feat:
pipeline_fcst_local() now accepts any object
supported by as_graph() and validates
key.
- feat:
PredictionFcst now stores explicit roles for
extra columns in $col_roles, replacing type-based detection
(#52).
- feat:
RecursiveForecaster now supports validation and
internal tuning (configure with set_validate()) and
delegates $importance(), $selected_features(),
and $oob_error() to the wrapped graph.
- feat:
TaskFcst now accepts character or integer keys,
while tsibble, tsf, and tsbox converters preserve their types.
- fix: Numeric
freq values now represent the seasonal
period, while the grid step is inferred from the order column.
- fix: Both forecasters no longer advertise learner properties they
cannot honour, fixing failures when tuning with
AutoTuner.
This drops the hotstart properties for both and additionally validation,
internal tuning, importance, selected features, and OOB error for
DirectForecaster.
- fix:
default_measures("fcst") now returns
regr.mse, so forecast resampling and benchmark results can
be aggregated without an explicit measure.
- fix:
DirectForecaster now rejects empty or duplicate
horizons values.
- fix:
fcst.arima, fcst.auto_adam,
fcst.ets, fcst.gum, fcst.rlgt,
and fcst.stlm parameter definitions now match the wrapped
functions’ defaults, ranges, and dependencies.
- fix:
fcst.mase, fcst.msis, and
fcst.rmsse now infer period from
task$freq unless it is set.
- fix:
fcst.nnetar now supports quantile predictions and
uses bootstrap, npaths, and innov
when simulating their prediction intervals.
- fix:
fcst.prophet now supports logistic growth through
a required cap task feature and an optional
floor task feature.
- fix:
fcst.sma now fits the complete training task and
no longer exposes the incompatible holdout parameter.
- fix:
fcst.tslm now omits season from its
generated default formula for nonseasonal tasks.
- fix:
PipeOpFcstAvg now declares its required packages
and the "fcst" tag instead of dropping them.
- fix:
rsmp("fcst.holdout", n = 0) now puts no
observations into the training set instead of all of them.
mlr3forecast 0.1.0