March 07 to 09 2016, Santa Clara, USA.


Speaker "Alexander Lavin" Details

Name :
alexander lavin
Company :
Title :
Research Engineer
Topic :

Detecting Anomalies in Streaming Data, Evaluating Algorithms for Real-world Use

Abstract :

Much of the world’s data is becoming streaming, time-series data, in domains such as finance, IT, security, medical, energy, and social media. Finding anomalies in time-series data can be critical for many applications, but how do we measure the effectiveness of anomaly detection algorithms for real-world use on streaming data? Traditional benchmarks are batch focused and do not apply to streaming applications. This calls for a scoring framework that does not allow lookahead, rewards early detection, and incorporates continuous learning. Fulfilling this need is the Numenta Anomaly Benchmark (NAB). The goal for NAB is to provide a controlled and repeatable environment of open-source tools to evaluate anomaly detection algorithms on streaming data. NAB includes a custom scoring methodology and a corpus of real-world, labeled data. These components are presented along with results and analyses for several open source, commercially-used algorithms.

Profile :

Software and research engineer at Numenta, building machine intelligence by reverse-engineering the neocortex; specializes in anomaly detection and natural language processing (NLP). Lavin studied mechanical engineering at Cornell and Carnegie Mellon Universities, focusing on spacecraft engineering.


Get latest updates of Global Data Science Conference
sent to your inbox.

Weekly insight from industry insiders.
Plus exclusive content and offers.