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SUMMARY:sktime - python toolbox for time series – introduction and new f
 eatures 2024: foundation models\, deep learning backends\, probabilistic m
 odels\, hierarchical demand forecasting\, marketplace features - Franz Kir
 aly\, Felipe Angelim\, Muhammad Armaghan Shakir\, Benedikt Heidrich
DTSTART;TZID=Europe/Berlin:20240827T160000
DTEND;TZID=Europe/Berlin:20240827T173000
DTSTAMP:20260813T213813Z
UID:pretalx-euroscipy-2024-DF3VHU@pretalx.com
DESCRIPTION:sktime is the most widely used scikit-learn compatible framewo
 rk library for learning with time series. sktime is maintained by a neutra
 l non-profit under permissive license\, easily extensible by anyone\, and 
 interoperable with the python data science stack.\n\nThis tutorial gives a
  hands-on introduction to sktime\, for common time series learning tasks s
 uch as forecasting\, starting with a general overview of the package and f
 orecasting interfaces for uni- and multivariate forecasts with endo-/exoge
 neous data\, probabilistic forecasts\, and forecasting in the presence of 
 hierarchical data.\n\nThe tutorial then proceeds to showcase some of the n
 ewest features in 2024\, based on a hierarchical demand forecasting use ca
 se example: support for foundation models\, hugging face connectors\, adva
 nced support for hierarchical and global forecasts\, and integration featu
 res for creating API compatible algorithms and sharing them via the sktime
  discoverability tools.
LOCATION:Room 5
URL:https://pretalx.com/euroscipy-2024/talk/DF3VHU/
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