CRAN v0.4.1

Bug fixes

• Fix indexing error of short-run exogenous regressors in the VECM() model.
• Fix generate() method for VECM() models producing array errors.

CRAN v0.4.0

New features

• Added generate() and IRF() methods for VAR models.
• Added IRF() method for ARIMA models.
• Added VECM() and VARIMA() models.

Improvements

• Documentation improvements.

CRAN v0.3.4

Small patch to resolve issues in C++ R headers.

Improvements

• Documentation improvements.

CRAN v0.3.3

Small patch to resolve CRAN check issues.

Improvements

• Documentation improvements.

Bug fixes

• Fixed generate(<ARIMA>) method for some variable names.
• Fixed df in generate(<TSLM>).

CRAN v0.3.2

Improvements

• Documentation improvements.
• Added approx_normal argument to forecast(<TSLM>). This allows you to
  optionally return forecasts from the more appropriate Student's T distribution
  instead of approximating to a Normal distribution. The default behaviour
  remains the same, which is to provide approximate Normal distribution
  forecasts which are nicer to work with in model combination and reconciliation
  (#343).
• ETS() will now ignore the smoothing parameter's range when specific
  parameter value is given (#317).
• Modified initial parameter values for ETS() when bounds = "admissible".
• Updated RW forecasts to use an unbiased estimate of sigma2 (#368).

Bug fixes

• Fixed issue with characteristic equation test for admissibility of ETS
  parameters (#341).
• Fixed ARIMA selecting differences that don't satisfy the order_constraint
  (#360).
• Fixed issue with forecasting ARIMA models with intercept and exogenous
  regressors.
• Fixed issue with VAR models not storing lagged regressor data for forecasting.

CRAN v0.3.1

Small release to resolve check issues with the development and patched versions
of R. The release includes some minor improvements to the output consistency of
initial states in ETS() models, the passing of arguments in ARIMA() models,
and handling of missing values in NNETAR().

Improvements

• Display of ETS initial states now use a state[t] notation to describe the
  state's position in time (#329, #261).
• Allowed specifying method argument in ARIMA() (#330).
• Improved handling of missing values in NNETAR() (#327).

Bug fixes

• Fixed error with forecasting and simulating from NNETAR() estimated using
  a short series (#326).
• Fixed AR() fitted values not being re-scaled to match original data (#318).

CRAN v0.3.0

The release of fabletools v0.3.0 introduced general support for computing h-step
ahead fitted values, using the hfitted(<mdl>, h = ???) function. This release
adds model-specific hfitted() support to ARIMA and ETS models for improved
performance and accuracy.

This release adds improved support for refitting models, largely in thanks to
contributions by @Tim-TU.

It is also now possible to specify an arbitrary model selection criterion
function for automatic ARIMA() model selection.

New features

• Added refit() method for NNETAR, MEAN, RW, SNAIVE, and NAIVE models
  (#287, #289, #321. @Tim-TU).
• Added hfitted() method for ETS and ARIMA, this allows fast estimation of
  h-step ahead fitted values.
• Added generate() method for AR, the forecast() method now supports
  bootstrap forecasting via this new method.

Improvements

• Added the selection_metric argument to ARIMA(), which allows more control
  over the measure used to select the best model. By default this function will
  extract the information criteria specified by the ic argument.
• Added trace argument for tracing the selection procedure used in ARIMA()

Bug fixes

• Fixed unnecessary warning when forecasting short horizons using NNETAR().
• Fixed generate() method for NNETAR models when data isn't scaled (#302).
• Fixed refit.ARIMA() re-selecting constant instead of using the provided
  model's constant usage.
• Fixed use of exogenous regressors in AR() models.

CRAN v0.2.1

This release coincides with v0.2.0 of the fabletools package, which contains
some substantial changes to the output of forecast() methods.
These changes to fabletools emphasise the distribution in the fable
object. The most noticeable is a change in column names of the fable, with the
distribution now stored in the column matching the response variable, and the
forecast mean now stored in the .mean column.
For a complete summary of these changes, refer to the fabletools v0.2.0 release
news: https://fabletools.tidyverts.org/news/index.html

New features

• Added the THETA() method.

Improvements

• Forecasts distributions are now provided by the distributional package. They
  are now more space efficient and allows calculation of distributional
  statistics including the mean(), median(), variance(), quantile(),
  cdf(), and density().
• The uncertainty of the drift parameter in random walk models (RW(),
  NAIVE() and SNAIVE()) is now included in data generated with generate().
• Added Syntetos-Boylan and Shale-Boylan-Johnston variants of CROSTON() method.
• Performance improvements.

Bug fixes

• Fixed issue with approximation being used when refitting ARIMA models and when
  a specific model is requested.
• Fixed glance() for TSLM() models when the data contains missing values.
• Fixed typo in glance() output of ETS() models.

Breaking changes

• The sample path means are now used instead of analytical means when forecasts
  are produced from sample paths.

CRAN v0.2.0

Improvements

• Added autoregressive modelling with AR().
• Better handling of rank deficiency in ARIMA().
• Added generate.ARIMA() method.
• Added bootstrap forecast paths for ARIMA() models.
• ARIMA() specials now allow specifying fixed coefficients via the fixed argument.
• Documentation improvements.

CRAN v0.1.2

Improvements

• Added CROSTON() for Croston's method of intermittent demand forecasting.
• Documentation improvements

Bug fixes

• Fixed NNETAR & VAR handling of missing values (#215).
• Fix ETS forecasting with forecast horizons less than the seasonal period (#219).
• Fixed season() special for non-seasonally based time indices (#220)
• Fix issue with simulation forecasting from damped ETS models.