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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

18,652

Version

0.5.0

License

GPL-3

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/tidyverts/fabletools

Homepage

https://fabletools.tidyverts.org/

Maintainer

Mitchell O'Hara-Wild

Last Published

September 17th, 2024

Functions in fabletools (0.5.0)

combination_weighted

Weighted combination

Create a dable object

fabletools-package

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

decomposition_model

Decomposition modelling

Create a feature set from tags

Internal vctrs methods

Directional accuracy measures

Create a fable object

components.mdl_df

Extract components from a fitted model

Extract fitted values from models

generate.mdl_df

Generate responses from a mable

percentile_score

Distribution accuracy measures

features_by_tag

Features by tag

Common exogenous regressors

Is the object a mable

Is the element an aggregation of smaller data

Minimum trace forecast reconciliation

Estimate a model

distribution_var

Return distribution variable

Construct a new set of forecasts

Interval estimate accuracy measures

hypothesize.mdl_df

Run a hypothesis test from a mable

Internal vctrs methods

Extract features from a dataset

interpolate.mdl_df

Interpolate missing values

Extract frequencies for common seasonal periods

forecast.mdl_df

Produce forecasts

Is the object a dable

A set of future scenarios for forecasting

features_by_pkg

Features by package

Provide a succinct summary of a model

new_model_class

Create a new class of models

Is the object a model

Forecast skill score measure

new_transformation

Create a new modelling transformation

Return model column variables

Create evaluation environment for specials

Middle out forecast reconciliation

Report information about an object

Estimate models

parse_model_lhs

Parse the RHS of the model formula for transformations

Forecast reconciliation

Point estimate accuracy measures

Extract the right hand side of a model

Extract the left hand side of a model

Helper special for producing a model matrix of exogenous regressors

Parse the model specification for specials

Is the object a fable

validate_formula

Validate the user provided model

parse_model_rhs

Parse the RHS of the model formula for specials

Internal vctrs methods

Objects exported from other packages

Identify outliers

Refit a mable to a new dataset

Create a new mable

register_feature

Register a feature function

Extract the response variable from a model

residuals.mdl_df

Extract residuals values from models

Extract model coefficients from a mable

Top down forecast reconciliation

Return response variables

Unpack a hilo column

Extend a fitted model with new data

Recursively traverse an object

Coerce a dataset to a mable

Compute Impulse Response Function (IRF)

aggregation-vctrs

Internal vctrs methods

Coerce to a dable object

Mean Arctangent Absolute Percentage Error

accuracy.mdl_df

Evaluate accuracy of a forecast or model

aggregate_index

Expand a dataset to include temporal aggregates

Create an aggregation vector

Expand a dataset to include other levels of aggregation

Coerce to a fable object

Bottom up forecast reconciliation

combination_ensemble

Ensemble combination

Box Cox Transformation

combination_model

Combination modelling

Bias adjust back-transformation functions

Augment a mable

autoplot.dcmp_ts

Decomposition plots

autoplot.tbl_ts

Plot time series from a tsibble

autoplot.fbl_ts

Plot a set of forecasts