NEWS.md
.
in a model formula for xreg
implemented with special_xreg()
will now include all measured variables (excluding the index and key variables).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).forecast()
, generate()
will now keep exogenous regressors in the output table.generics::forecast()
for better compatibility with registering methods alongside other packages (#375).hypothesize()
generic for running statistical tests on a trained model.combination_weighted()
function for producing a combination model with arbitrary weights.type = "innovation"
.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.outliers()
generic for identifying the outliers of a fitted model.special_xreg()
special generator, for producing a model matrix of exogenous regressors. It supports an argument for controlling the default inclusion of an intercept.common_xregs
helper from fable to fabletools for providing a common and consistent interface for common time series exogenous regressors.features()
functions if the .index
argument is used in the function.fitted(h > 1)
method (#302).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).quantile_score()
, which is similar to percentile_score()
except it allows a set of quantile probs
to be provided (#280).autoplot(<dable>)
. If the decomposition provides distributions for its components, then the uncertainty of the components will be plotted with interval ribbons.generate()
.fitted(<mable>, h > 1)
.as_fable(<forecast>)
for converting older forecast
class objects to fable
data structures.top_down(method = "forecast_proportion")
for reconciliation using the forecast proportions techniques.middle_out()
forecast reconciliation method.MDA()
, MDV()
and MDPV()
(#273, @davidtedfordholt).fill_gaps(<fable>)
.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)..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.response
variable in as_fable()
will no longer error, it now sets the provided response
value as the distribution’s new response.autoplot()
are now always grouped by the data’s key.bottom_up()
aggregation mismatch for redundant leaf nodes (#266).min_trace()
reconciliation for degenerate hierarchies (#267).select(<mable>)
not keeping required key variables (#297)....
not being passed through in report()
.bottom_up()
forecast reconciliation method.skill_score()
accuracy measure modifier.agg_vec()
for manually producing aggregation vectors.augment()
, tidy()
and glance()
) with model methods (such as forecast()
and generate()
).agg_vec
classes, aggregated values will now always match regardless of the value used.summarise()
with a fable will now retain the fable class if the distribution still exists under the same variable name.as_fable.forecast()
to convert forecast objects from the forecast package to work with fable.CRPS()
performance when using sampling distributions (#240).features()
(#258).future.apply()
to parallelize forecast()
when the future
package is attached (#268).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
.View()
panel.aggregate_key()
can now be used with non-syntactic variable names.refit()
dropping reconciliation attributes (#251).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
.RMSSE()
, pinball_loss()
, scaled_pinball_loss()
.mable_vars()
), response variables (response_vars()
) and distribution variables (distribution_var()
).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.progressr::with_progress()
function. Progress will no longer be displayed automatically during lengthy calculations.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.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.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.as_mable
, as_dable
, and as_fable
have been changed to accept character vectors for specifying common attributes (such as response variables, and distributions).models
argument for mable
and as_mable
has been replaced with model
for consistency with the lack of plural in key
.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()
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.show_gap
option not working when more than one forecast is plotted.autolayer()
plotting issues due to inherited aesthetics.aggregate_key()
no longer drops keys, instead they are kept as forecast()
producing forecasts via h
when new_data
does not include a given series (#202).xreg()
can now be called directly as a special.accuracy.fbl_ts()
error when certain names were used in the fable.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).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.select.mdl_df()
usage with negative select values (#120).features()
for a tsibble with key variables but only one series.stream()
causing issues with subsequent methods (#144).min_trace()
reconciliation (@GeorgeAthana).CRPS()
) accuracy measure.scale(value)
to be used.min_trace(method = "wls_struct")
) forecast reconciliation (@GeorgeAthana).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.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
).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
).new_model_class()
, new_model_definition()
) and decomposition definitions (new_decomposition_class()
, new_decomposition_definition()
).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()
).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.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.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.new_transformation()
), and bias adjustment (bias_adjust()
) methods.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
.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()
.augment()
, tidy()
, and glance()
.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.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.fitted()
, model residuals with residuals()
, and the response variable with response()
. These functions return a tsibble (tbl_ts
) object.refit()
, which allows an estimated model to be applied to a new dataset.report()
, which provides a detailed summary of an estimated model.generate()
support, which is used to simulate future paths from an estimated model.stream()
, which allows an estimated model to be extended using newly available data.interpolate()
, which allows missing values from a dataset to be interpolated using an estimated model (and model appropriate interpolation strategy).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.feature_set()
, which allows a collection of registered features from loaded packages to be accessed using a tagging system.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.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.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.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.