Name: Forecasting Time Series with Linear Regression: A Feature-Driven Approach
Start: 2025-07-03T18:00:00+02:00
End: 2025-07-03T20:00:00+02:00

Curious about time series forecasting but not sure where to start?

Join us for a beginner-friendly workshop exploring how linear regression can be used for time series forecasting.

This talk explores ***the fundamentals of time series forecasting using linear regression models***. We’ll cover how to transform time series data into a supervised learning format and utilize feature engineering techniques to model various components of the series, including trends, seasons, outliers, and structural breaks.

No prior time series experience is required—just a basic understanding of regression and a desire to learn.

🧠 **What you’ll learn:**

* How to frame 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

Whether you’re brushing up on regression or starting your forecasting journey, this workshop will provide hands-on insights in an accessible way.

Federica Gazzelloni

Rafaela Lucas

R-Ladies Rome

R-Ladies

RConsortium

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**Rami Krispin** leads the Data Science and Engineering team at Apple Finance – Services and Infrastructure, where he focuses on transforming complex business challenges into scalable data products. His team leverages advanced statistical methods and machine learning models to support Apple leadership in making informed, data-driven decisions.

Rami is also an active open-source contributor and the author of *Hands-On Time Series Analysis with R* (Packt). He has developed several R packages for time series analysis and machine learning, and is currently working on his next book, *Applied Time Series Analysis and Forecasting*, which explores forecasting at scale.

R-Ladies Rome

Forecasting Time Series with Linear Regression: A Feature-Driven Approach

R-Ladies Rome

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