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()

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 equation interpolate components augment glance tidy hypothesize generate refit forecast hilo guide_level

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