Unsupervised real-time anomaly detection and root cause estimation by Aitor Landete and Pablo Mateos

Nowadays, an increasing number of business problems rely on the analysis of real-time metrics. Typical use cases range from credit fraud detection to predictive maintenance. Also, we are moving towards an era where all sensors and devices are connected to the internet, I.e. IoT, which monitor the performance of different KPIs. For this reason, it is crucial to extend and refine real-time analytics to streaming data sources to reach fast-developing sectors such as: Smart Cities, Industry 4.0, Smart Healthcare, etc. In this talk we will focus on unsupervised real-time anomaly detection. For this type of setups, it is a standard practice to set up thresholds for the detection of anomalies.
#BIGTH20 #AI #Analytics #IoT #MachineLearning #DeepLearning #DataScience
Session presented at Big Things Conference 2010 by Aitor Landete, Data Scientist at Telefónica and Pablo Mateos, Data Scientist at Telefónica
17th November 2020
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