CRAN v0.3.0

New features

• Added scenarios() function for providing multiple scenarios to the
  new_data argument. This allows different sets of future exogenous regressors
  to be provided to functions like forecast(), generate(), and
  interpolate() (#110).
• Added quantile_score(), which is similar to percentile_score() except it
  allows a set of quantile probs to be provided (#280).
• Added distribution support for autoplot(<dable>). If the decomposition
  provides distributions for its components, then the uncertainty of the
  components will be plotted with interval ribbons.
• Added block bootstrap option for bootstrapping innovations in generate().
• Added multiple step ahead fitted values support via fitted(<mable>, h > 1).
• Added as_fable(<forecast>) for converting older forecast class objects to
  fable data structures.
• Added top_down(method = "forecast_proportion") for reconciliation using the
  forecast proportions techniques.
• Added middle_out() forecast reconciliation method.
• Added directional accuracy measures, including MDA(), MDV() and MDPV()
  (#273, @davidtedfordholt).
• Added fill_gaps(<fable>).

Improvements

• The pinball_loss() and percentile_score() accuracy measures are now scaled
  up by 2x for improved meaning. The loss at 50% equals absolute error and the
  average loss equals CRPS (#280).
• Automatic transformation functions formals are now named after the response
  variable and not converted to .x, preventing conflicts with values named .x.
• box_cox() and inv_box_cox() are now vectorised over the transformation
  parameter lambda.
• RMSSE() accuracy measure is now included in default accuracy() measures.
• Specifying a different response variable in as_fable() will no longer
  error, it now sets the provided response value as the distribution's new
  response.
• Minor vctrs support improvements.

Bug fixes

• Data lines in fable autoplot() are now always grouped by the data's key.
• Fixed bottom_up() aggregation mismatch for redundant leaf nodes (#266).
• Fixed min_trace() reconciliation for degenerate hierarchies (#267).
• Fixed select(<mable>) not keeping required key variables (#297).
• Fixed ... not being passed through in report().

CRAN v0.2.1

New features

• Added bottom_up() forecast reconciliation method.
• Added the skill_score() accuracy measure modifier.
• Added agg_vec() for manually producing aggregation vectors.

Improvements

• Fixed some inconsistencies in key ordering of model accessors (such as
  augment(), tidy() and glance()) with model methods (such as forecast()
  and generate()).
• Improved equality comparison of agg_vec classes, aggregated values will now
  always match regardless of the value used.
• Using summarise() with a fable will now retain the fable class if the
  distribution still exists under the same variable name.
• Added as_fable.forecast() to convert forecast objects from the forecast
  package to work with fable.
• Improved CRPS() performance when using sampling distributions (#240).
• Reconciliation now works with hierarchies containing aggregate leaf nodes,
  allowing unbalanced hierarchies to be reconciled.
• Produce unique names for unnamed features used with features() (#258).
• Documentation improvements
• Performance improvements, including using future.apply() to parallelize
  forecast() when the future package is attached (#268).

Breaking changes

• The residuals obtained from the augment() function are no longer controlled
  by the type argument. Response residuals (y - yhat) are now always found
  in the .resid column, and innovation residuals (the model's error) are now
  found in the .innov column. Response residuals will differ from innovation
  residuals when transformations are used, and if the model has non-additive
  residuals.
• dist_*() functions are now removed, and are completely replaced by the
  distributional package. These are removed to prevent masking issues when
  loading packages.
• fortify(<fable>) will now return a tibble with the same structure as the
  fable, which is more useful for plotting forecast distributions with the
  ggdist package. It can no longer be used to extract intervals from the
  forecasts, this can be done using hilo(), and numerical values from a
  <hilo> can be extracted with unpack_hilo() or interval$lower.

Bug fixes

• Fixed issue with aggregated date vectors (#230).
• Fixed display of models in View() panel.
• Fixed issue with combination models not inheriting vctrs functionality (#237).
• aggregate_key() can now be used with non-syntactic variable names.
• Added tsibble cast methods for fable and dable objects, fixing issues with
  tidyverse functionality between datasets of different column orders (#247).
• Fixed refit() dropping reconciliation attributes (#251).

CRAN v0.2.0

New features

• Distributions are now provided by the distributional package, which is more
  space efficient and allows calculation of distributional statistics including
  the mean(), median(), variance(), quantile(), cdf() and density().
• autoplot.fbl_ts() and autolayer.fbl_ts() now accept the point_forecast
  argument, which is a named list of functions that describe the method used to
  obtain the point forecasts. If multiple are specified, each method will be
  identified using the linetype.
• Added accuracy measures: RMSSE(), pinball_loss(), scaled_pinball_loss().
• Added accessor functions for column names (or metadata) of interest. This
  includes models in a mable (mable_vars()), response variables
  (response_vars()) and distribution variables (distribution_var()).
• Added support for combinations of non-normal forecasts, which produces mean
  point forecasts only.
• Added support for reconciling non-normal forecasts, which produces reconciled
  point forecasts only.

Improvements

• Improved dplyr support. You can now use bind_*() and *_join() operations
  on mables, dables, and fables. More verbs are supported by these extension
  data classes, and so behaviour should work closer to what is expected.
• Progress reporting is now handled by the progressr package. This allows you to
  decide if, when, and how progress is reported. To show progress, wrap your
  code in the progressr::with_progress() function. Progress will no longer be
  displayed automatically during lengthy calculations.
• Improved support for streaming data to models with transformed response
  variables.
• hilo.fbl_ts() now keeps existing columns of a fable.
• forecast() will now return an empty fable instead of erroring when no
  forecasts are requested.
• is_aggregated() now works for non-aggregated data types.
• Documentation improvements.

Breaking changes

• The fable returned by forecast() now stores the distribution in the column
  named the response variable (previously, this was the point forecast). Point
  forecasts are now stored in the .mean column, which can be customised using
  the point_forecast argument.
• The bias_adjust option for forecast() is replaced by point_forecast,
  allowing you to specify which point forecast measures to display (fable/#226).
  This has been done to reduce confusion around the argument's usage,
  disambiguate the returned point forecast's meaning, and also allow users
  to specify which (if any) point forecasts to provide.
• The data coercion functions as_mable, as_dable, and as_fable have been
  changed to accept character vectors for specifying common attributes (such as
  response variables, and distributions).
• The models argument for mable and as_mable has been replaced with model
  for consistency with the lack of plural in key.
• Intervals from multivariate distributions are now returned as data frames of
  hilo intervals. The columns are the response variables. Similar structures
  are returned when computing other distributional statistics like the mean.
• hilo intervals can no longer be unnested as they are now stored more
  efficiently as a vctrs record type. The unpack_hilo() function will continue
  to function as expected, and you can now obtain the components of the interval
  with x$lower, x$upper, and x$level,
• rbind() methods are deprecated in favour of bind_rows()
• The row order of wide to long mable operations (such as accuracy()) has
  changed (due to shift to pivot_longer() from gather()). Model column name
  values are now nested within key values, rather than key values nested in
  model name values.

Bug fixes

• Fixed show_gap option not working when more than one forecast is plotted.
• Fixed autolayer() plotting issues due to inherited aesthetics.
• aggregate_key() no longer drops keys, instead they are kept as .
• Forecast reconciliation now works with historical data that is not temporally
  aligned.
• Fixed forecast() producing forecasts via h when new_data does not
  include a given series (#202).

CRAN v0.1.3

Improvements

• Better support for tidyverse packages using vctrs.
• Performance improvements for reconciliation and parsing.
• xreg() can now be called directly as a special.

Bug fixes

• Fixed accuracy.fbl_ts() error when certain names were used in the fable.

CRAN v0.1.2

Improvements

• Added MAAPE accuracy measure.
• Added support for exogenous regressors in decomposition models.
• Added support for generating data from combination models.
• Forecast plots via autoplot.fbl_ts() and autolayer.fbl_ts() now support
  the show_gap argument. This can be used to connect the historical observations
  to the forecasts (#113).

Breaking changes

• Decompositions are now treated as models.
  To access the decomposed values, you will now have to use components().
  For example, tourism %>% STL(Trips) is now tourism %>% model(STL(Trips)) %>% components().
  This change allows for more flexible decomposition specifications, and better interfaces for decomposition modelling.

Bug fixes

• Fixed select.mdl_df() usage with negative select values (#120).
• Fixed features() for a tsibble with key variables but only one series.
• Fixed interpolated values not being back transformed (tidyverts/fable#202).
• Fixed stream() causing issues with subsequent methods (#144).

CRAN v0.1.1

Breaking changes

• Updated method names available for min_trace() reconciliation (@GeorgeAthana).

Improvements

• Improved error messaging for failing features.
• Added Continuous Ranked Probability Score (CRPS()) accuracy measure.
• Transformations of features are now computed for separately for each key, allowing transformations such as scale(value) to be used.
• Added structural scaling method for MinT (min_trace(method = "wls_struct")) forecast reconciliation (@GeorgeAthana).
• Performance improvements.
• Documentation improvements.

Bug fixes

• Added failure condition for disjoint reconciliation graphs.

CRAN v0.1.0

• First release.

New features

Data structures

• Added the mable (model table) data class (mdl_df) which is a tibble-like data structure for applying multiple models to a dataset. Each row of the mable refers to a different time series from the data (identified by the key columns). A mable must contain at least one column of time series models (mdl_ts), where the list column itself (lst_mdl) describes how these models are related.
• Added the fable (forecast table) data class (fbl_ts) which is a tsibble-like data structure for representing forecasts. In extension to the key and index from the tsibble (tbl_ts) class, a fable (fbl_ts) must contain columns of point forecasts for the response variable(s), and a single distribution column (fcdist).
• Added the dable (decomposition table) data class (dcmp_ts) which is a tsibble-like data structure for representing decompositions. This data class is useful for representing decompositions, as its print method describes how its columns can be combined to produce the original data, and has a more appropriate autoplot() method for displaying decompositions. Beyond this, a dable (dcmp_ts) behaves very similarly to a tsibble (tbl_ts).

Modelling

• Support for model (new_model_class(), new_model_definition()) and decomposition definitions (new_decomposition_class(), new_decomposition_definition()).
• Added parsing tools to compactly specify models using a formula interface. Transformations specified on left hand side, where the response variable is determined by object length. In case of a conflict in object length, such as GDP/CPI, the response will be the ratio of the pair. To transform a variable by some other data variable, the response can be specified using resp(), giving resp(GDP)/CPI. Multiple variables (and separate transformations for each), can be specified using vars(): vars(log(GDP), CPI). The inputs to the model are specified on the right hand side, and are handled using model defined specials (new_specials()).
• Added methods to train a model definition to a dataset. model() is the recommended interface, which can fit many model definitions to each time series in the input dataset returning a mable (mdl_df). The lower level interface for model estimation is accessible using estimate() which will return a time series model (mdl_ts), however using this interface is discouraged.

Forecasting

• Added forecast(), which allows you to produce future predictions of a time series from fitted models. The methods provided in fabletools handle the application of new data (such as the future index or exogenous regressors) to model specials, giving a simple and consistent interface to forecasting any model. The forecast methods will automatically backtransform and bias adjust any transformations specified in the model formula. This function returns a fable (fbl_ts) object.
• Added a forecast distribution class (fcdist) which is used to describe the distribution of forecasts. Common forecast distributions have been added to the package, including the normal distribution (dist_normal()), multivariate normal (dist_mv_normal()) and simulated/sampled distributions (dist_sim()). In addition to this, dist_unknown() is available for methods that don't support distributional forecasts. A new distribution can be added using the new_fcdist() function. The forecast distribution class handles transformations on the distribution, and is used to create forecast intervals of the hilo class using the hilo() function. Mathematical operations on the normal distribution are supported.
• Added tools for working with transformations in models, including automatic back-transformation, transformation classes (new_transformation()), and bias adjustment (bias_adjust()) methods.
• Added aggregate_key(), which is used to compute all levels of aggregation in a specified key structure. It supports nested structures using parent / key and crossed structures using keyA * keyB.
• Added support for forecast reconciliation using reconcile(). This function modifies the way in which forecasts from a model column are combined to give coherent forecasts. In this version the MinT (min_trace()) reconciliation technique is available. This is commonly used in combination with aggregate_key().

Generics

• Added broom package functionality for augment(), tidy(), and glance().
• Added components(), which returns a dable (dcmp_ts) that describes how the fitted values of a model were obtained from its components. This is commonly used to visualise the states of a state space model.
• Added equation(), which returns a formatted display of a fitted model's equation. This is commonly used to conveniently add model equations to reports, and to better understand the structure of the model.
• Added accessors to common model data elements: fitted values with fitted(), model residuals with residuals(), and the response variable with response(). These functions return a tsibble (tbl_ts) object.
• Added refit(), which allows an estimated model to be applied to a new dataset.
• Added report(), which provides a detailed summary of an estimated model.
• Added generate() support, which is used to simulate future paths from an estimated model.
• Added stream(), which allows an estimated model to be extended using newly available data.
• Added interpolate(), which allows missing values from a dataset to be interpolated using an estimated model (and model appropriate interpolation strategy).
• Added features(), along with scoped variants features_at(), features_if() and features_all(). These functions make it easy to compute a large collection of features for each time series in the input dataset.
• Added feature_set(), which allows a collection of registered features from loaded packages to be accessed using a tagging system.

Models

• Added decomposition_model(), which allows the components from any decomposition method that returns a dable (dcmp_ts) to be modelled separately and have their forecasts combined to give forecasts on the original response variable.
• Added combination_model(), which allows any model to be combined with any other. This function accepts a function which describes how the models are combined (such as combination_ensemble()). A combination model can also be obtained by using mathematical operations on model definitions or estimated models.
• Added null_model(), which can be used as a empty model in a mable (mdl_df). This is most commonly used as a substitute for models which encountered an error, preventing the successfully estimated models from being lost.

Evaluation

• Added accuracy(), which allows the accuracy of a model to be evaluated. This function can be used to summarise model performance on the training data (accuracy.mdl_df(), accuracy.mdl_ts()), or to evaluate the accuracy of forecasts over a test dataset (accuracy.fbl_ts()). Several accuracy measures are supported, including point_accuracy_measures (ME, MSE, RMSE, MAE, MPE, MAPE, MASE, ACF1), interval_accuracy_measures (winkler_score) and distribution_accuracy_measures (percentile_score). These accuracy functions can be used in conjunction with the rolling functions in the tsibble package (stretch_tsibble(), slide_tsibble(), tile_tsibble()) to computed time series cross-validated accuracy measures.