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Kishan Manani- Backtesting and error metrics for modern time series forecasting | PyData London 2024

Автор: PyData

Загружено: 2024-06-21

Просмотров: 6426

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PyData
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Backtesting and error metrics for modern time series forecasting

Evaluating time series forecasting models for modern use cases has become incredibly challenging. This is because modern forecasting problems often involve a large number of related time series, often hierarchical, with a diverse set of characteristics such as intermittency, non-normality, and non-stationarity. In this talk we'll discuss all the tips, tricks, and pitfalls in creating model evaluation strategies and error metrics to overcome these challenges.

Slides: https://github.com/KishManani/PyDataL...

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.

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Kishan Manani- Backtesting and error metrics for modern time series forecasting | PyData London 2024

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