Function reference • fabletools

Overview Describes the package
`fabletools` `fabletools-package`	fabletools: Core Tools for Packages in the 'fable' Framework
Data structures Data classes for models, forecasts and decompositions.
`mable()`	Create a new mable
`as_mable()`	Coerce a dataset to a mable
`is_mable()`	Is the object a mable
`fable()`	Create a fable object
`as_fable()`	Coerce to a fable object
`is_fable()`	Is the object a fable
`dable()`	Create a dable object
`as_dable()`	Coerce to a dable object
`is_dable()`	Is the object a dable
`mable_vars()`	Return model column variables
`response_vars()`	Return response variables
`distribution_var()`	Return distribution variable
Models Models which make use of models from other packages, or are useful for programming.
`decomposition_model()`	Decomposition modelling
`combination_model()`	Combination modelling
`combination_ensemble()`	Ensemble combination
`combination_weighted()`	Weighted combination
`null_model()` `is_null_model()`	NULL model
Forecast reconciliation Use reconciliation techniques to ensure that forecasts are coherent with the hierarchical structure of data.
`reconcile()`	Forecast reconciliation
`min_trace()`	Minimum trace forecast reconciliation
`bottom_up()`	Bottom up forecast reconciliation
`middle_out()`	Middle out forecast reconciliation
`top_down()`	Top down forecast reconciliation
`aggregate_key()`	Expand a dataset to include other levels of aggregation
`aggregate_index()`	Expand a dataset to include temporal aggregates
`agg_vec()`	Create an aggregation vector
`is_aggregated()`	Is the element an aggregation of smaller data
Accuracy evaluation Functionality for evaluating model performance
`accuracy(<mdl_df>)` `accuracy(<mdl_ts>)` `accuracy(<fbl_ts>)`	Evaluate accuracy of a forecast or model
`ME()` `MSE()` `RMSE()` `MAE()` `MPE()` `MAPE()` `MASE()` `RMSSE()` `ACF1()` `point_accuracy_measures`	Point estimate accuracy measures
`MAAPE()`	Mean Arctangent Absolute Percentage Error
`winkler_score()` `pinball_loss()` `scaled_pinball_loss()` `interval_accuracy_measures`	Interval estimate accuracy measures
`percentile_score()` `quantile_score()` `CRPS()` `distribution_accuracy_measures`	Distribution accuracy measures
`MDA()` `MDV()` `MDPV()` `directional_accuracy_measures`	Directional accuracy measures
`skill_score()`	Forecast skill score measure
Methods The fabletools package facilitates the handling of key structures for these generics.
`reexports` `%>%` `as_tsibble` `vars` `autoplot` `autolayer` `accuracy` `equation` `interpolate` `components` `augment` `glance` `tidy` `hypothesize` `generate` `refit` `forecast` `hilo`	Objects exported from other packages
`model()`	Estimate models
`report()`	Report information about an object
`stream()`	Extend a fitted model with new data
`outliers()`	Identify outliers
`model_sum()`	Provide a succinct summary of a model
`forecast(<mdl_df>)` `forecast(<mdl_ts>)`	Produce forecasts
`generate(<mdl_df>)` `generate(<mdl_ts>)`	Generate responses from a mable
`interpolate(<mdl_df>)` `interpolate(<mdl_ts>)`	Interpolate missing values
`refit(<mdl_df>)` `refit(<mdl_ts>)`	Refit a mable to a new dataset
`augment(<mdl_df>)` `augment(<mdl_ts>)`	Augment a mable
`glance(<mdl_df>)` `glance(<mdl_ts>)`	Glance a mable
`tidy(<mdl_df>)` `coef(<mdl_df>)` `tidy(<mdl_ts>)` `coef(<mdl_ts>)`	Extract model coefficients from a mable
`components(<mdl_df>)` `components(<mdl_ts>)`	Extract components from a fitted model
`fitted(<mdl_df>)` `fitted(<mdl_ts>)`	Extract fitted values from models
`residuals(<mdl_df>)` `residuals(<mdl_ts>)`	Extract residuals values from models
`estimate()`	Estimate a model
`response()`	Extract the response variable from a model
`scenarios()`	A set of future scenarios for forecasting
`hypothesize(<mdl_df>)` `hypothesize(<mdl_ts>)`	Run a hypothesis test from a mable
`is_model()`	Is the object a model
Features Functions for using and defining features across a dataset.
`features()` `features_at()` `features_all()` `features_if()`	Extract features from a dataset
`feature_set()`	Create a feature set from tags
`register_feature()`	Register a feature function
Transformations Commonly used transformation functions.
`box_cox()` `inv_box_cox()`	Box Cox Transformation
`new_transformation()` `invert_transformation()`	Create a new modelling transformation
Graphics Some `autoplot()` and `autolayer()` methods are defined for classes commonly used within fabletools.
`autoplot(<tbl_ts>)` `autolayer(<tbl_ts>)`	Plot time series from a tsibble
`autoplot(<fbl_ts>)` `autolayer(<fbl_ts>)`	Plot a set of forecasts
`autoplot(<dcmp_ts>)`	Decomposition plots
Extension package helpers Functions provided to help develop extension packages.
`special_xreg()`	Helper special for producing a model matrix of exogenous regressors
`new_specials()`	Create evaluation environment for specials
`new_model_class()` `new_model_definition()`	Create a new class of models
`model_lhs()`	Extract the left hand side of a model
`model_rhs()`	Extract the right hand side of a model
`common_periods` `common_periods.default` `common_periods.tbl_ts` `common_periods.interval` `get_frequencies` `get_frequencies.numeric` `get_frequencies.NULL` `get_frequencies.character` `get_frequencies.Period`	Extract frequencies for common seasonal periods
`common_xregs`	Common exogenous regressors

Reference

Overview

Data structures

Models

Forecast reconciliation

Accuracy evaluation

Methods

Features

Transformations

Graphics

Extension package helpers