generate() and IRF() methods for VAR
models.IRF() method for ARIMA models.VECM() and VARIMA() models.Small patch to resolve issues in C++ R headers.
Small patch to resolve CRAN check issues.
generate(<ARIMA>) method for some variable
names.generate(<TSLM>).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).ETS() when bounds
= “admissible”.order_constraint (#360).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().
state[t]
notation to describe the state’s position in time (#329, #261).method argument in
ARIMA() (#330).NNETAR()
(#327).NNETAR() estimated using a short series (#326).AR() fitted values not being re-scaled to match
original data (#318).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.
refit() method for NNETAR, MEAN, RW, SNAIVE, and
NAIVE models (#287, #289, #321. @Tim-TU).hfitted() method for ETS and ARIMA, this allows
fast estimation of h-step ahead fitted values.generate() method for AR, the
forecast() method now supports bootstrap forecasting via
this new method.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.trace argument for tracing the selection
procedure used in ARIMA()NNETAR().generate() method for NNETAR models when data
isn’t scaled (#302).refit.ARIMA() re-selecting constant instead of
using the provided model’s constant usage.AR() models.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
THETA() method.mean(),
median(), variance(), quantile(),
cdf(), and density().RW(), NAIVE() and SNAIVE()) is
now included in data generated with generate().CROSTON() method.glance() for TSLM() models when the
data contains missing values.glance() output of ETS()
models.AR().ARIMA().generate.ARIMA() method.ARIMA() models.ARIMA() specials now allow specifying fixed
coefficients via the fixed argument.CROSTON() for Croston’s method of intermittent
demand forecasting.MEAN() model
(#203).MEAN() model (#204).ARIMA,
ETS, TSLM, MEAN, RW,
NAIVE, SNAIVE, NNETAR,
VAR.