06-06, 11:00–12:30 (Europe/London), Doddington Forum
This hands-on workshop covers how to use open source ML models like LSTMs and TimeSeries LLM's, with Python to try to forecast weather patterns, with best practices for data preparation and real time predictions.
Weather patterns are notoriously challenging to predict, typically requiring sophisticated satellite technology and advanced modeling techniques. However, recent advancements in deep learning for time series forecasting offer powerful new methods to tackle this complexity.
In this hands-on workshop, you will learn to try to forecast weather conditions for the next six months using Python, Google Colab, InfluxDB and popular libraries like Neural Prophet and state of the art Time Series LLMs. Learn the strengths, weaknesses, and common pitfalls of each approach, from classical techniques (ARIMA) to using Transformers. We’ll explore data preprocessing, model training, evaluation, with practical examples and ready-to-use notebooks. All code and instructions will be available on GitHub, ensuring you can continue exploring time series forecasting beyond the session.
Previous knowledge expected
Suyash Joshi is an accomplished engineer and developer advocate at InfluxData, with previous roles at Oracle and RingCentral. Holding a B.S. in Computer Science and an M.A. in Game Design, he merges technical expertise with creativity. He is dedicated to community building, delivering talks & workshops globally while sharing his knowledge and connecting with others.