BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//pretalx.com//pydata-london-2026//talk//EQZ7VK
BEGIN:VTIMEZONE
TZID:GMT
BEGIN:STANDARD
DTSTART:20001029T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000326T020000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-pydata-london-2026-EQZ7VK@pretalx.com
DTSTART;TZID=GMT:20260606T102000
DTEND;TZID=GMT:20260606T110500
DESCRIPTION:Sensors operating in complex environments produce noisy data. D
 etermining exactly when a system transitions between states — and what v
 alues it is recording — is surprisingly hard: vibrations\, environmental
  changes\, and gradual shifts all conspire against simple threshold approa
 ches. This talk walks through a real-world Python pipeline that solves thi
 s problem\, starting with classical signal processing\, exposing its failu
 re modes\, and then building a principled solution using a Kalman filter f
 or noise reduction coupled with a Hidden Markov Model (HMM) for state infe
 rence. Attendees will leave understanding how to frame sensor problems as 
 state estimation tasks and how to apply these techniques in Python using n
 ecessary libraries.
DTSTAMP:20260602T223433Z
LOCATION:Hardwick Hub
SUMMARY:From Noisy Sensors to Events: Event Detection in Sensor data with K
 alman Filters and Hidden Markov Models - Ono Gantsog
URL:https://pretalx.com/pydata-london-2026/talk/EQZ7VK/
END:VEVENT
END:VCALENDAR