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UID:pretalx-pyconde-pydata-2024-H3X3AX@pretalx.com
DTSTART;TZID=CET:20240423T114000
DTEND;TZID=CET:20240423T121000
DESCRIPTION:In this talk\, we address the Cold Start problem in Demand Fore
 casting\, focusing on scenarios where historical data is scarce or nonexis
 tent. This constitutes a common situation in practice\, such as with the l
 aunch of new products in Retail. However\, many Time Series and Machine Le
 arning models encounter difficulties in handling this challenge\, primaril
 y due to their dependence on a substantial amount of historical data for e
 ffective training and prediction.\n\nWe begin by providing an overview of 
 established techniques used to address the Cold Start problem\, including 
 methods like padding\, feature engineering\, and leveraging item similarit
 ies. Additionally\, we explore more recent advancements and emerging resea
 rch\, such as Transfer Learning for Time Series.\n\nWhile each technique p
 resents its unique set of trade-offs\, the challenge lies in determining t
 he most suitable approach for a given dataset or use case. This aspect is 
 often not widely understood\, and our goal is to unravel this complexity b
 y offering practical insights. Furthermore\, we introduce a practical fram
 ework for systematically evaluating different forecasting strategies withi
 n the Cold Start setting\, guiding you in selecting the most suitable appr
 oach for your datasets and use cases.
DTSTAMP:20260411T022405Z
LOCATION:B09
SUMMARY:Tackling the Cold Start Challenge in Demand Forecasting - Alexander
  Meier\, Daria Mokrytska
URL:https://pretalx.com/pyconde-pydata-2024/talk/H3X3AX/
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