Minor patch to build package with latest R version as requested by CRAN.
Minor patch for upcoming release of ggdist v3.3.1
Improvements
- Added (scaled) pinball loss metrics to
interval_accuracy_measures (#379).
- Improved use of random seed in parallel modelling and forecasting (#384).
- Documentation improvements
Improvements
- Improved handling of
combination_model() when used with transformed component models.
-
autoplot(<fbl_ts>), autolayer(<fbl_ts>) and autoplot(<dcmp_ts>) now use the ggdist package visualising uncertainty with distributional vectors.
New features
- The formula parser now identifies and stores length 1 values in the transformation environment. This simplifies common tasks like automatic box-cox parameters for each series, which can now be done with
fable::ARIMA(box_cox(y, feasts::guerrero(y))).
Improvements
- Added support for visualising different point forecasts (say means and medians) when only one forecast is to be plotted for each series.
Improvements
- Fixed handling of transformed distributions which accept a parameter from the dataset.
-
. in a model formula for xreg implemented with special_xreg() will now include all measured variables (excluding the index and key variables).
- Improved handling of transformations with forecast sample distributions.
- Added support for reconciling sample paths.
-
accuracy(<fbl_ts>) can now summarise accuracy over key variables. This is done by specifying the accuracy by argument and not including some (or all) of the fable’s key variables (#341).
- Like
forecast(), generate() will now keep exogenous regressors in the output table.
- Re-export
generics::forecast() for better compatibility with registering methods alongside other packages (#375).
Improvements
- The fallback residuals() method now handles transformations when
type = "innovation".
- Improved supported expressions for producing combination models. The appropriate response variable is now simplified for all functions that produce that original response variable. This notably includes
0.7*mdl1 + 0.3*mdl2 - if mdl1 and mdl2 are models with the same response variables, then the resulting combination model will also have the same response variable.
- Documentation improvements.
Bug fixes
- Fixed issue with exogenous regressors (
xreg) in reconciliation methods that partially forecast the hierarchy.
- Fixed issue with keys being dropped when several
mdl_df (mable) objects were combined.
New features
- Added
outliers() generic for identifying the outliers of a fitted model.
- Added
special_xreg() special generator, for producing a model matrix of exogenous regressors. It supports an argument for controlling the default inclusion of an intercept.
- Migrated
common_xregs helper from fable to fabletools for providing a common and consistent interface for common time series exogenous regressors.
- Added experimental support for passing the tsibble index to
features() functions if the .index argument is used in the function.
Improvements
- Added transformation support for fallback
fitted(h > 1) method (#302).
- Documentation improvements.
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().
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).
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).
Improvements
- Better support for tidyverse packages using vctrs.
- Performance improvements for reconciliation and parsing.
-
xreg() can now be called directly as a special.
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).
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.
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.