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UID:pretalx-pyconde-pydata-2026-KQM8JJ@pretalx.com
DTSTART;TZID=CET:20260416T150500
DTEND;TZID=CET:20260416T153500
DESCRIPTION:The rise of time-series foundation models like Chronos-2 and Ti
 mesFM has sparked a debate: can a single pre-trained model replace the spe
 cialized "local" models we have tuned for years? We moved beyond the hype 
 to test these models in production-like environments\, from high-level mar
 ket trends to granular article-level demand. In this talk\, we share a tra
 nsparent look at our journey: the zero-shot capabilities of these models\,
  the reality of fine-tuning with exogenous business drivers\, and a compar
 ison between generative models and state-of-the-art classical methods. We 
 categorize what is currently possible\, what remains a challenge\, and pro
 vide a roadmap for teams looking to integrate foundation models into their
  forecasting stack without sacrificing reliability.
DTSTAMP:20260502T114418Z
LOCATION:Ferrum [2nd Floor]
SUMMARY:Foundation Models in Forecasting: Are We There Yet? Lessons from th
 e Trenches - Dr. Irena Bojarovska
URL:https://pretalx.com/pyconde-pydata-2026/talk/KQM8JJ/
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