Tidy time series forecasting with
tidymodels
.
For those that prefer video tutorials, we have an 11minute YouTube Video that walks you through the Modeltime Workflow.
(Click to Watch on YouTube)
Getting Started with
Modeltime:
A walkthrough of the 6Step Process for using modeltime
to
forecast
Modeltime
Documentation:
Learn how to use modeltime
, find Modeltime Models, and
extend modeltime
so you can use new algorithms inside the
Modeltime Workflow.
CRAN version:
install.packages("modeltime", dependencies = TRUE)
Development version:
remotes::install_github("businessscience/modeltime", dependencies = TRUE)
Modeltime unlocks time series models and machine learning in one framework
No need to switch back and forth between various frameworks. modeltime
unlocks machine learning & classical time series analysis.
arima_reg()
,
arima_boost()
, & exp_smoothing()
).prophet_reg()
&
prophet_boost()
)parsnip
model: rand_forest()
,
boost_tree()
, linear_reg()
, mars()
, svm_rbf()
to forecastA streamlined workflow for forecasting
Modeltime incorporates a streamlined workflow (see Getting Started with Modeltime) for using best practices to forecast.
A streamlined workflow for forecasting
Learn a growing ecosystem of forecasting packages
The modeltime ecosystem is growing
Modeltime is part of a growing ecosystem of Modeltime forecasting packages.
Modeltime is an amazing ecosystem for time series forecasting. But it can take a long time to learn:
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Modeltime

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