Extending broom for time series forecasting
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sweep
Extending
broomto time series forecasting
The
sweeppackage extends the
broomtools (tidy, glance, and augment) for performing forecasts and time series analysis in the “tidyverse”. The package is geared towards “tidying” the forecast workflow used with Rob Hyndman’s
forecastpackage.
Benefits
- Designed for modeling and scaling forecasts using the the
tidyverse
tools in R for Data Science - Extends
broom
for model analysis (ARIMA, ETS, BATS, etc) - Tidies the
forecast
objects for easy plotting and “tidy” data manipulation - Integrates
timetk
to enable dates and datetimes (irregular time series) in the tidied forecast output
Tools
The package contains the following elements:
-
model tidiers:
sw_tidy
,sw_glance
,sw_augment
,sw_tidy_decomp
functions extendtidy
,glance
, andaugment
from thebroom
package specifically for models (ets()
,Arima()
,bats()
, etc) used for forecasting. -
forecast tidier:
sw_sweep
converts aforecast
object to a tibble that can be easily manipulated in the “tidyverse”.
Making forecasts in the tidyverse
sweepenables converting a
forecastobject to
tibble. The result is ability to use
dplyr,
tidyr, and
ggplotnatively to manipulate, analyze and visualize forecasts.
Forecasting multiple time series groups at scale
Often forecasts are required on grouped data to analyse trends in sub-categories. The good news is scaling from one time series to many is easy with the various
sw_functions in combination with
dplyrand
purrr.
Forecasting multiple models for accuracy
A common goal in forecasting is to compare different forecast models against each other.
sweephelps in this area as well.
broom extensions for forecasting
If you are familiar with
broom, you know how useful it is for retrieving “tidy” format model components.
sweepextends this benefit to the
forecastpackage workflow with the following functions:
-
sw_tidy
: Returns model coefficients (single column) -
sw_glance
: Returns accuracy statistics (single row) -
sw_augment
: Returns residuals -
sw_tidy_decomp
: Returns seasonal decompositions -
sw_sweep
: Returns tidy forecast outputs.
The compatibility chart is listed below.
| Object | sw_tidy() | sw_glance() | sw_augment() | sw_tidy_decomp() | sw_sweep() | | :---------- | :--------: | :----------: | :-----------: | :----------------: | :---------: | | ar | | | | | | | arima | X | X | X | | | | Arima | X | X | X | | | | ets | X | X | X | X | | | robets | X | X | X | X | | | baggedETS | | | | | | | bats | X | X | X | X | | | tbats | X | X | X | X | | | nnetar | X | X | X | | | | stl | | | | X | | | HoltWinters | X | X | X | X | | | StructTS | X | X | X | X | | | tslm | X | X | X | | | | decompose | | | | X | | | adf.test | X | X | | | | | Box.test | X | X | | | | | kpss.test | X | X | | | | | forecast | | | | | X |
Function Compatibility
Installation
Here’s how to get started.
Development version with latest features:
# install.packages("devtools")
devtools::install_github("business-science/sweep")
Further Information
The
sweeppackage includes several vignettes to help users get up to speed quickly:
- SW00 - Introduction to
sweep
- SW01 - Forecasting Time Series Groups in the tidyverse
- SW02 - Forecasting Using Multiple Models