Foundation Models in Forecasting: Are We There Yet? Lessons from the Trenches @PyDataTV
Foundation Models in Forecasting: Are We There Yet? Lessons from the Trenches  @PyDataTV
Uploaded August 2026 | Updated September 2026, 2 weeks ago
🔊 Recorded at PyCon DE & PyData 2026, 16.04.2026
2026.pycon.de/talks/KQM8JJ

🎓 Watch Dr. Irena Bojarovska share a hype-free, production-tested look at whether time-series foundation models can truly replace specialized local models in forecasting.

Speakers:
Dr. Irena Bojarovska

Description:
Zero-shot forecasting aims to predict time-series data without model training, utilizing foundation models to handle multivariate settings and global forecasting across multiple markets and KPIs. Traditional statistical models and tree-based methods often struggle with consistency across related variables, such as the relationship between gross merchandise volume, item count, and average price.

Testing with Chronos 2 demonstrated a 4.6 percentage point improvement in weighted average percentage error for GMV compared to baselines. The model's success stems from time and group attention layers and training on synthetic data that captures causal relationships. This allows the model to maintain consistency across different KPIs and markets without the need for fine-tuning, provided that past and future covariates are integrated.

Production readiness requires evaluating five pillars: accuracy, stability, consistency, exogenous sensitivity, and scalability. While zero-shot capabilities are effective for aggregate-level forecasting and cold-start scenarios, the models currently lack scalability for article-level forecasting and do not provide native explainability or feature importance. Effective implementation remains dependent on high-quality data preparation and the careful selection of covariates.

⭐️ About PyCon DE:
PyCon DE is the leading conference on open-source Python applications in AI and data science. It brings together industry professionals, researchers, AI and data science practitioners, and software engineering communities, providing a unique platform for collaboration, knowledge sharing, and innovation.

The PyCon DE & PyData 2026 conference delivered an exceptional experience, fostering stronger connections within the Python community while showcasing the latest advancements in artificial intelligence and data science. Attendees enjoyed a diverse and engaging program of talks, workshops, and networking opportunities, further establishing the conference as a premier event for Python, AI, and data science enthusiasts across Germany.

PyCon DE 2027 will take place in Heidelberg from 19 to 23 April 2027.

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Links:
• Conference website: pycon.de
• Other sessions: 2026.pycon.de/talks

The conference was organized by
• Python Softwareverband e.V.: pysv.org
• Pioneers Hub gemeinnützige GmbH: pioneershub.org
in collaboration with NumFOCUS Inc.: numfocus.org


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Acknowledgements:
Special thanks to all the volunteers and sponsors who made this event possible.

About:
Python Softwareverband e.V.:
PySV is a non-profit that promotes the use and development of Python in Germany through events, education, and advocacy, fostering an open Python community.

Pioneers Hub gemeinnĂźtzige GmbH:
is a non-profit fostering innovation in AI and tech by connecting experts and promoting knowledge exchange through events and collaborative initiatives.

NumFOCUS Inc.
supports open-source scientific computing by providing financial and logistical support to key projects like NumPy and Jupyter, promoting sustainable development and collaboration.


pydata.org

PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
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Foundation Models in Forecasting: Are We There Yet? Lessons from the Trenches

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