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fabletools

The R package fabletools provides tools for building modelling packages, with a focus on time series forecasting. This package allows package developers to extend fable with additional models, without needing to depend on the models supported by fable.

Installation

You could install the stable version on CRAN:

install.packages("fabletools")

You can install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("tidyverts/fabletools")

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Version

Install

install.packages('fabletools')

Monthly Downloads

15,111

Version

0.4.1

License

GPL-3

Issues

84

Pull Requests

5

Stars

95

Forks

36

Maintainer

Mitchell O'Hara-Wild

Last Published

March 2nd, 2024

Functions in fabletools (0.4.1)

components.mdl_df

Extract components from a fitted model
construct_fc

Construct a new set of forecasts
distribution_var

Return distribution variable
fitted.mdl_df

Extract fitted values from models
estimate

Estimate a model
percentile_score

Distribution accuracy measures
common_periods

Extract frequencies for common seasonal periods
forecast.mdl_df

Produce forecasts
generate.mdl_df

Generate responses from a mable
hypothesize.mdl_df

Run a hypothesis test from a mable
interpolate.mdl_df

Interpolate missing values
is_model

Is the object a model
box_cox

Box Cox Transformation
dable

Create a dable object
features_by_pkg

Features by package
features_by_tag

Features by tag
dable-vctrs

Internal vctrs methods
fable-vctrs

Internal vctrs methods
mable-vctrs

Internal vctrs methods
glance.mdl_df

Glance a mable
decomposition_model

Decomposition modelling
MDA

Directional accuracy measures
new_model_class

Create a new class of models
model_rhs

Extract the right hand side of a model
fabletools-package

fabletools: Core Tools for Packages in the 'fable' Framework
fable

Create a fable object
is_fable

Is the object a fable
winkler_score

Interval estimate accuracy measures
is_mable

Is the object a mable
new_specials

Create evaluation environment for specials
parse_model

Parse the model specification for specials
ME

Point estimate accuracy measures
residuals.mdl_df

Extract residuals values from models
outliers

Identify outliers
new_transformation

Create a new modelling transformation
response_vars

Return response variables
null_model

NULL model
model_sum

Provide a succinct summary of a model
reconcile

Forecast reconciliation
report

Report information about an object
register_feature

Register a feature function
scenarios

A set of future scenarios for forecasting
mable_vars

Return model column variables
mable

Create a new mable
unpack_hilo

Unpack a hilo column
response

Extract the response variable from a model
features

Extract features from a dataset
feature_set

Create a feature set from tags
is_aggregated

Is the element an aggregation of smaller data
validate_formula

Validate the user provided model
refit.mdl_df

Refit a mable to a new dataset
top_down

Top down forecast reconciliation
is_dable

Is the object a dable
model

Estimate models
tidy.mdl_df

Extract model coefficients from a mable
stream

Extend a fitted model with new data
model_lhs

Extract the left hand side of a model
traverse

Recursively traverse an object
reexports

Objects exported from other packages
min_trace

Minimum trace forecast reconciliation
middle_out

Middle out forecast reconciliation
parse_model_lhs

Parse the RHS of the model formula for transformations
parse_model_rhs

Parse the RHS of the model formula for specials
special_xreg

Helper special for producing a model matrix of exogenous regressors
skill_score

Forecast skill score measure
as_fable

Coerce to a fable object
aggregate_index

Expand a dataset to include temporal aggregates
aggregate_key

Expand a dataset to include other levels of aggregation
as_mable

Coerce a dataset to a mable
accuracy.mdl_df

Evaluate accuracy of a forecast or model
as_dable

Coerce to a dable object
MAAPE

Mean Arctangent Absolute Percentage Error
agg_vec

Create an aggregation vector
augment.mdl_df

Augment a mable
aggregation-vctrs

Internal vctrs methods
bottom_up

Bottom up forecast reconciliation
combination_ensemble

Ensemble combination
combination_model

Combination modelling
common_xregs

Common exogenous regressors
combination_weighted

Weighted combination
autoplot.tbl_ts

Plot time series from a tsibble
autoplot.dcmp_ts

Decomposition plots
autoplot.fbl_ts

Plot a set of forecasts
bias_adjust

Bias adjust back-transformation functions