Forecasting with regression models

This workshop focuses on the foundation of feature-based forecasting with linear regression using R. The workshop is beginner-friendly, and it covers the following topics:
- How to frame a time series as a regression problem
- Feature engineering techniques for time-aware data
- Modeling trend, seasonality, outliers, and breaks
- Practical tips for evaluation and validation

This workshop is for practitioners (data analysts/scientists) who wish to learn how to forecast with regression models. It assumes no background in time series analysis and forecasting, but assumes basic knowledge of probability, linear regression, and R programming.

Instructor: Rami Krispin

Rami Krispin is a data science and engineering manager who mainly focuses on time series analysis, forecasting, and MLOps applications.

He is passionate about open source, working with data, machine learning, and putting stuff into production. He creates content about MLOps and recently released a course - Data Pipeline Automation with GitHub Actions Using R and Python, on LinkedIn Learning, and is the author of Hands-On Time Series Analysis with R.