modeltime: Time series forecasting in R with tidymodels

An introduction to our forecasting package, #modeltime. Modeltime extends the tidymodels ecosystem for time series forecasting. Learn how to forecast with #ARIMA, #prophet, and linear regression #timeseries models. GET THE CODE SHOWN IN THE VIDEO: 📰 Free R-Tips Newsletter (FREE R GitHub Code Access): https://learn.business-science.io/r-tips-newsletter 📺 Set Up Your R-Tips Codebase (GitHub Video) https://youtu.be/F7aYV0RPyD0 Are you struggling to learn time series forecasting? Do you need to make a career transition? Would you like to become the forecasting expert for your organization? Then read on... 🚀 MY COURSES WILL SKYROCKET YOUR CAREER IN WEEKS: 🆓FREE R-TRACK MASTERCLASS: https://learn.business-science.io/free-rtrack-masterclass 📘 High-Performance Time Series Forecasting | DS4B 203-R (Learn Modeltime, TimeTK, Modeltime Gluonts, Modeltime H2O, and more) https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting/ 📚The 5-Course R-Track Program (Full System, Beginner to Expert, 12-18 months FASTER than other online programs) https://university.business-science.io/p/5-course-bundle-machine-learning-web-apps-time-series/ TABLE OF CONTENTS 00:00 Introduction to Modeltime 00:30 GitHub Project Setup 01:03 Libraries: Modeltime & Tidymodels 01:48 Data: DC Bike Sharing Daily 02:59 Train/Test Split 03:39 Forecasting (is Exciting!) 03:49 ARIMA (Automatic) 04:40 Prophet 05:24 GLMNET (Machine Learning) 06:32 Modeltime Workflow 06:46 Modeltime Table & Modeltime Calibrate 07:32 Modeltime Accuracy 08:12 Modeltime Forecast (Visualize Test Set) 09:00 Modeltime Refit & Forecast (Visualize Future Forecast) 09:42 How to Learn More!