utsf 1.3.1

• 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.

utsf 1.3.0

• 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.

utsf 1.2.0

• 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.

utsf 1.1.0

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

utsf 1.0.0

• Initial CRAN submission.