Changes in version 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.

                 Changes in version 1.3.2 (2026-04-14)                  

  - 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.

                 Changes in version 1.3.1 (2025-10-23)                  

  - 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.

                 Changes in version 1.3.0 (2025-07-08)                  

  - 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.

                        Changes in version 1.2.1                        

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

                 Changes in version 1.2.0 (2025-05-19)                  

  - 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.

                 Changes in version 1.1.0 (2024-12-10)                  

  - Improvements in estimation of forecast accuracy with rolling origin
    evaluation.
  - The way in which pre-processings are specified has changed.
  - Method plot.utsf is implemented.
  - Linear models (stats::lm) are supported.

                 Changes in version 1.0.0 (2024-10-14)                  

  - Initial CRAN submission.