modeltime
The time series forecasting package for the tidymodels ecosystem.
Tutorials

Getting Started with
Modeltime:
A walkthrough of the 6Step Process for usingmodeltime
to
forecast 
Modeltime
Documentation:
Learn how to usemodeltime
, find Modeltime Models, and
extendmodeltime
so you can use new algorithms inside the
Modeltime Workflow.
Installation
Install the CRAN version:
install.packages("modeltime")
Or, install the development version:
remotes::install_github("businessscience/modeltime")
Features & Benefits
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.
 forecast: Use ARIMA, ETS, and more models coming (
arima_reg()
,
arima_boost()
, &exp_smoothing()
).  prophet: Use Facebook’s Prophet algorithm (
prophet_reg()
&
prophet_boost()
)  tidymodels: Use any
parsnip
model:rand_forest()
,
boost_tree()
,linear_reg()
,mars()
,svm_rbf()
to forecast
A streamlined workflow for forecasting
Modeltime incorporates a simple workflow (see Getting Started with
Modeltime)
for using best practices to forecast.
A streamlined workflow for forecasting
Learning More
My Talk on HighPerformance Time Series
Forecasting
Time series is changing. Businesses now need 10,000+ time series
forecasts every day. This is what I call a HighPerformance Time
Series Forecasting System (HPTSF)  Accurate, Robust, and Scalable
Forecasting.
HighPerformance Forecasting Systems will save companies MILLIONS of
dollars. Imagine what will happen to your career if you can provide
your organization a “HighPerformance Time Series Forecasting System”
(HPTSF System).
I teach how to build a HPTFS System in my HighPerformance Time Series
Forecasting Course. If interested in learning Scalable
HighPerformance Forecasting Strategies then take my
course.
You will learn:
 Time Series Machine Learning (cuttingedge) with
Modeltime
 30+
Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)  NEW  Deep Learning with
GluonTS
(Competition Winners)  Time Series Preprocessing, Noise Reduction, & Anomaly Detection
 Feature engineering using lagged variables & external regressors
 Hyperparameter Tuning
 Time series crossvalidation
 Ensembling Multiple Machine Learning & Univariate Modeling
Techniques (Competition Winner)  Scalable Forecasting  Forecast 1000+ time series in parallel
 and more.