NEWS

utsf 1.3.3 (2026-04-22)

The param parameter in the regression function provided by the user is changed to a ... parameter.
Extreme gradient boosting is supported using the xgboost package.

The model tree is now built with the parameter committees set to 5
The param parameter used to set arguments to the regression models in the create_model function is no longer needed, you can set the arguments directly.

The pre-processing for dealing with trending series is now specified in a simpler way.
The vignette has been improved.
The way in which tuning parameters are specified to user's models has changed.

The lags argument in the function for building the model (now create_model) now can be an unordered integer vector.
The lags argument in the function for building the model (now create_model) now must be an integer vector.
match.arg() is used so the options are visible to the user in the help.
A main change is that the functionality of the forecast function, that did a lot of things, is now distributed in several functions: create_model() (build the model), forecast() (do the forecasts), efa() for assessing forecast accuracy and tune_grid() for parameter tuning.
Prediction intervals are optionally computed.

The default value of parameter transform_features in trend function is again TRUE.

The estimated forecast accuracy per horizon is also computed.
Now it is possible to use only 1 lag with additive or multiplicative transformation, if the features are not transformed.
Now it is possible to transform only the target (and not the features) with the multiplicative transformation.
An error is produced if a too large autorregresive lag is used.
An error is produced in method KNN when k is greater than the size of the training set.
A warning is produced when the time series is too short to estimate forecast accuracy.