BEGIN:VCALENDAR VERSION:2.0 PRODID:-//PYVOBJECT//NONSGML Version 1//EN BEGIN:VTIMEZONE TZID:Europe/Brussels BEGIN:STANDARD DTSTART:20001029T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:20260524T223010Z - 6747@eu441a.odoo.com DTSTART;TZID=Europe/Brussels:20260421T130000 DTEND;TZID=Europe/Brussels:20260421T170000 CREATED:20260524T223010Z DESCRIPTION:Masterclass Timeseries Forecastin g with GenAI\nBuild your own forecaster with GenAI at minimal cost and with minimal expertise required Forecasting sits at the heart of proactiv e decision making\, but for many organizations\, it has long felt out of r each. Traditional methods often require deep technical expertise\, expensi ve tools\, and complex infrastructure. Meanwhile\, companies generate endl ess streams of sensor\, production\, and operational data that remain larg ely untapped\, simply because turning numbers into insight is hard. In thi s three hour master class\, we change that. You will discover how anyone\, from maintenance managers to data owners\, can transform raw time series data into dependable forecasts using modern generative AI\, or GenAI. By h arnessing free foundation models and today’s coding assistants\, we dram atically lower the barrier to entry. You will learn how to build a high qu ality forecasting solution in a single afternoon\, without writing code an d without paying for costly licenses. Our mission is simple\, to put the p ower of forecasting into the hands of industry practitioners like you\, so you can make smarter decisions\, reduce uncertainty\, and move innovation forward with confidence. What you will learn in this intensive half day s essionUnderstand how time series forecasting works in practice\, including the metrics that matter most for evaluating forecast quality Decide when classical machine learning approaches are sufficient and when generative A I offers a real advantage Explain why modern foundation models such as Chr onos-2\, TimesFM and PatchTST perform so well on forecasting tasks Use cod ing assistants like Claude Code and Codex to work faster and more effectiv ely\, while understanding their limitations and safety considerations Get started immediately with a concrete\, step-by-step recipe provided by Sirr isInstructors Mihail Mihaylov is a GenAI expert and entrepreneur with 18 y ears of [...] DTSTAMP:20260524T223010Z LOCATION:VBO-FEB\, Ravensteinstraat 4\, Brussels 1000 SUMMARY:Masterclass Timeseries Forecasting with GenAI X-ALT-DESC;FMTTYPE=text/html:Masterclass Time series Forecasting with GenAI\nBuild your own forecaster with GenAI at minimal cost and with minimal expertise required Forecasting sits at the heart of proactive decision making\, but for many organizations\, it has l ong felt out of reach. Traditional methods often require deep technical ex pertise\, expensive tools\, and complex infrastructure. Meanwhile\, compan ies generate endless streams of sensor\, production\, and operational data that remain largely untapped\, simply because turning numbers into insigh t is hard. In this three hour master class\, we change that. You will disc over how anyone\, from maintenance managers to data owners\, can transform raw time series data into dependable forecasts using modern generative AI \, or GenAI. By harnessing free foundation models and today’s coding ass istants\, we dramatically lower the barrier to entry. You will learn how t o build a high quality forecasting solution in a single afternoon\, withou t writing code and without paying for costly licenses. Our mission is simp le\, to put the power of forecasting into the hands of industry practition ers like you\, so you can make smarter decisions\, reduce uncertainty\, an d move innovation forward with confidence. What you will learn in this int ensive half day sessionUnderstand how time series forecasting works in pra ctice\, including the metrics that matter most for evaluating forecast qua lity Decide when classical machine learning approaches are sufficient and when generative AI offers a real advantage Explain why modern foundation m odels such as Chronos-2\, TimesFM and PatchTST perform so well on forecast ing tasks Use coding assistants like Claude Code and Codex to work faster and more effectively\, while understanding their limitations and safety co nsiderations Get started immediately with a concrete\, step-by-step recipe provided by SirrisInstructors Mihail Mihaylov is a GenAI expert and entre preneur with 18 years of [...] END:VEVENT END:VCALENDAR