business-science/ sweep

forecast

tidyverse

prediction

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business-science

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Description

Extending broom for time series forecasting

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sweep

Name: sweep
Brand: sweep
SKU: //business-science/sweep
Rating: 2.095 (138 reviews)

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Extending
broom
to time series forecasting

The

sweep

package extends the

broom

tools (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

forecast

package.

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 extend
```
tidy
```
,
```
glance
```
, and
```
augment
```
from the
```
broom
```
package specifically for models (
```
ets()
```
,
```
Arima()
```
,
```
bats()
```
, etc) used for forecasting.
forecast tidier:
```
sw_sweep
```
converts a
```
forecast
```
object to a tibble that can be easily manipulated in the “tidyverse”.

Making forecasts in the tidyverse

sweep

enables converting a

forecast

object to

tibble

. The result is ability to use

dplyr

tidyr

, and

ggplot

natively 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

dplyr

and

purrr

Forecasting multiple models for accuracy

A common goal in forecasting is to compare different forecast models against each other.

sweep

helps 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.

sweep

extends this benefit to the

forecast

package 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 | :---------- | ar | | arima | X | Arima | X | ets | X | robets | X | baggedETS | | bats | X | tbats | X | nnetar | X | stl | | HoltWinters | X | StructTS | X | tslm | X | decompose | | adf.test | X | Box.test | X | kpss.test | X | forecast | | sw_tidy() | sw_glance() | sw_augment() | sw_tidy_decomp() | sw_sweep() | | :--------: | :----------: | :-----------: | :----------------: | :---------: | | | | | | | X | X | | | | X | X | | | | X | X | X | | | X | X | X | | | | | | | | X | X | X | | | X | X | X | | | X | X | | | | | | X | | | X | X | X | | | X | X | X | | | X | X | | | | | | X | | | X | | | | | X | | | | | X | | | | | | | | 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

sweep

package 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