fable 0.3.1

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).

fable 0.3.0

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.

fable 0.2.1

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.

fable 0.2.0

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.

fable 0.1.2

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.

fable 0.1.1

Improvements

• Added interpolation method for MEAN() model (#203).
• Added rolling mean option for MEAN() model (#204).

Bug fixes

• Corrected forecast standard error for drift models.

fable 0.1.0

• First release.

New features

• Support for 9 models and relevant methods: ARIMA, ETS, TSLM, MEAN, RW, NAIVE, SNAIVE, NNETAR, VAR.