BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//london2024.pydata.org//cfp//TC7ZFJ
BEGIN:VTIMEZONE
TZID:Europe/London
BEGIN:STANDARD
DTSTART:20001029T020000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000326T010000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-cfp-AKS33W@london2024.pydata.org
DTSTART;TZID=Europe/London:20240615T163000
DTEND;TZID=Europe/London:20240615T171000
DESCRIPTION:`functime` is a modern time-series forecasting library to **gen
 erate predictions for thousands of time series at once\, while never leavi
 ng your laptop**. Thanks to Polars' powerful query engine\, feature extrac
 tion and cross-validation are **1-2 orders of magnitude faster**. Plus\, `
 functime` offers a best-of-the-class set of diagnostic tools to further st
 reamline your workflow.\n\nIn this talk\, we'll learn how to use `functime
 ` to analyse your model and generate blazingly fast prediction intervals u
 sing EnBPI\, a state-of-the-art conformal prediction framework that is als
 o available in other popular Python packages.
DTSTAMP:20250709T215845Z
LOCATION:Warwick
SUMMARY:Uncertainty estimation at scale with functime\, Polars and conforma
 l predictions - Luca Baggi
URL:https://london2024.pydata.org/cfp/talk/AKS33W/
END:VEVENT
END:VCALENDAR