A gentle 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 time series models in this short video tutorial.
This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks.
Here are the links to get set up. 👇
Modeltime Video Tutorial
Learn how to forecast with Modeltime in under 10-minutes.
Learn how to use
modeltime in our 10-minute YouTube video tutorial.
Get the Code
The video tutorial comes with 100-lines of code that takes you step-by-step through a introductory forecasting with:
Auto ARIMA: An automatic forecasting algorithm that ships with
Prophet: A forecasting algorithm made by Facebook that works well with daily data (and also ships with
Linear Regression (Penalized): A machine learning approach to time series forecasting that is made available with
It gets better
You’ve just scratched the surface, here’s what’s coming…
The Modeltime Ecosystem functionality is much more feature-rich than what we’ve covered here (I couldn’t possibly cover everything in this post). 😀
Here’s what I didn’t cover:
Feature Engineering: We can make this forecast much more accurate by including features from competition-winning strategies
Ensemble Modeling: We can stack H2O Models with other models not included in H2O like GluonTS Deep Learning.
Deep Learning: We can use GluonTS Deep Learning for developing high-performance, scalable forecasts.
So how are you ever going to learn time series analysis and forecasting?
You’re probably thinking:
- There’s so much to learn
- My time is precious
- I’ll never learn time series
I have good news that will put those doubts behind you.
You can learn time series analysis and forecasting in hours with my state-of-the-art time series forecasting course. 👇
Advanced Time Series Course
Become the times series expert in your organization.
My Advanced Time Series Forecasting in R course is available now. You’ll learn
modeltime plus the most powerful time series forecasting techniques available like GluonTS Deep Learning. Become the times series domain expert in your organization.
You will learn:
- Time Series Foundations – Visualization, Preprocessing, Noise Reduction, & Anomaly Detection
- Feature Engineering using lagged variables & external regressors
- Hyperparameter Tuning – For both sequential and non-sequential models
- Time Series Cross-Validation (TSCV)
- Ensembling Multiple Machine Learning & Univariate Modeling Techniques (Competition Winner)
- Deep Learning with GluonTS (Competition Winner)
- and more.
Project Roadmap, Future Work, and Contributing to Modeltime
Modeltime is a growing ecosystem of packages that work together for forecasting and time series analysis. Here are several useful links:
Modeltime Ecosystem Roadmap on GitHub – See the past development and future trajectory. Did we miss something? Make a suggestion.
Business Science data science blog – I announce all Modeltime Software happenings
Have questions about Modeltime?
Make a comment in the chat below. 👇
And, if you plan on using
modeltime for your business, it’s a no-brainer: Join my Time Series Course.