Abhishek Murthy - Backtesting Time Series Forecasting Algorithms in SKTime and SKForecast

www.pydata.org Backtesting refers to the process of evaluating a time series forecasting algorithm on historical data by replicating the corresponding real-world scenario. Concurrently, parameters such as the model updating and retraining frequencies are also tuned based on the usecase and relevant computational constraints. In this talk, we will review the backtesting of time series algorithms using sktime and skforecast. The two open-source machine learning libraries are popular options for developing and deploying forecasting models. Specifically, the following aspects will be covered. • Comparing and contrasting backtesting-related features of the two libraries • An overview of the different types of cross-validation schemes for time series forecasting, including expanding and fixed windows • Model update and retraining for both direct and recursive multistep forecasts The talk is geared toward data scientists that want to systematically evaluate time series forecasting models in varied settings. In addition to gaining an overview of the various aspects, the audience will also learn about the implementation options supported by the two libraries. No prior knowledge about machine learning algorithms for forecasting is needed to attend the talk. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps