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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/PyDataLondon2024
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