Description
From sensor data to epidemic outbreaks, particle dynamics to environmental monitoring, much of crucial real world data has temporal nature. Fundamental challenges facing data specialist dealing with time series include not only predicting the future values, but also determining when these values are alarming. Standard anomaly detection algorithms and common rule-based heuristics often fall short in addressing this problem effectively. In this talk, we will closely examine this domain, exploring its unique characteristics and challenges. You will learn to apply some of the most promising techniques for detecting time series anomalies as well as relevant scientific Python tools that can help you with it.