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🎥 Forecasting Time Series with Linear Regression: A Feature-Driven Approach Recorded live at R-Ladies Rome, this beginner-friendly session with @RamiKrispinDS explores how to transform time series data into a supervised learning problem. You’ll learn how to engineer features that capture trends, seasonality, outliers, and structural breaks—using nothing more than linear regression. @RamiKrispinDS leads the Data Science and Engineering team at Apple Finance – Services and Infra, where he helps transform complex business questions into data-driven solutions using machine learning and statistical modeling. He’s also an open-source contributor and the author of Hands-On Time Series Analysis with R. 👩💻 Perfect for those new to time series or eager to strengthen their forecasting toolkit. 🔗 Slides & resources: https://github.com/RamiKrispin/r-ladi... 🌐 Learn more: Rami’s Book: https://ramikrispin.github.io/atsaf/ Rami’s Book Hands-On Time Series Analysis with R: Perform time series analysis and forecasting using R: https://github.com/RamiKrispin https://www.amazon.com/dp/1788629159 📝 Blog Post: https://rladiesrome.org 📅 Stay updated: https://www.meetup.com/rladies-rome 0:00 R-Ladies Rome Introduction 02:55 Rami Krispin's talk: Forecasting Time Series with Linear Regression: A Feature-Driven Approach 12:35 Data 26:44 Time Series Decomposition 49:36 Correlation Analysis 58:58 Seasonal Analysis 1:06:00 Forecasting with Linear Regression #rstats #timeseries #datascience #rladies #forecasting #opensource #machinelearning #ramikrispin