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UID:pretalx-cfp-89NLCS@vermont2024.pydata.org
DTSTART;TZID=US/Eastern:20240729T111500
DTEND;TZID=US/Eastern:20240729T114500
DESCRIPTION:Backtesting refers to the process of evaluating a time series f
 orecasting algorithm on historical data by replicating the corresponding r
 eal-world scenario.  Concurrently\, parameters such as the model updating 
 and retraining frequencies are also tuned based on the usecase and relevan
 t computational constraints.\n\nIn this talk\, we will review the backtest
 ing of time series algorithms using sktime and skforecast.  The two open-s
 ource machine learning libraries are popular options for developing and de
 ploying forecasting models.  Specifically\, the following aspects will be 
 covered.\n\n• Comparing and contrasting backtesting-related features of 
 the two libraries\n\n• An overview of the different types of cross-valid
 ation schemes for time series forecasting\, including expanding and fixed 
 windows\n\n• Model update and retraining for both direct and recursive m
 ultistep forecasts\n\nThe talk is geared toward data scientists that want 
 to systematically evaluate time series forecasting models in varied settin
 gs.  In addition to gaining an overview of the various aspects\, the audie
 nce will also learn about the implementation options supported by the two 
 libraries.  No prior knowledge about machine learning algorithms for forec
 asting is needed to attend the talk.
DTSTAMP:20250709T214946Z
LOCATION:Filmhouse
SUMMARY:Backtesting Time Series Forecasting Algorithms in SKTime and SKFore
 cast - Abhishek Murthy
URL:https://vermont2024.pydata.org/cfp/talk/89NLCS/
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