Statistical distances are distances between distributions or data samples and are used in a variety of machine learning applications. In this talk, we will show how we use SciPy's statistical distance functions—some of which we recently contributed—to design powerful and production-ready anomaly detection algorithms. With visual illustrations, we will describe the inner workings and the properties of a few common statistical distances and explain what makes them convenient to use, yet powerful to solve various problems. We will also show real-life applications and concrete examples of the anomalous patterns that such algorithms are able to detect in performance-monitoring and business-metric time series.

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