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fable (development version)

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 refit() method for NNETAR (#287, @Tim-TU).
  • Added refit() method for MEAN (#289, @Tim-TU).
  • Added generate() method for AR, the forecast() method now supports bootstrap forecasting via this new method.

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.