This talk will frame the topic of time series forecasting in the language of machine learning. This framing will be used to introduce the skits library which provides a scikit-learn-compatible API for fitting and forecasting time series models using supervised machine learning. Finally, a real-world deployment of skits involving thousands of forecasts per hour will be demonstrated.
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