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SkillsCast

Anomaly Detection: A breakdown of Twitter’s Seasonal Hybrid ESD

29th April 2017 in London at CodeNode

There are 19 other SkillsCasts available from Data Science Festival 2017

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In a world of deep learning statistical techniques are out of fashion but can still be very effective tools. Twitter’s open source anomaly detection project uses a statistical technique call Seasonal Hybrid ESD.

In this talk you will go through the various steps in the algorithm from data preparation and time series decomposition through to finding potentially multiple anomalies. The technique could be used to spot deviations from behavioural patterns with the benefit that it is easy to see why an anomaly is unusual.

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Anomaly Detection: A breakdown of Twitter’s Seasonal Hybrid ESD

Peter Tillotson

Peter has been researching and solving leading-edge distributed computational problems for nearly 20 years. This began with intelligent agent systems; he tracked high-performance computing and their developments in both Grid and Cloud. More recently Peter has been closely following and working with Big Data, MapReduce, NoSQL and realtime streaming analysis. Peter is a Data Scientist, Trainer and Researcher who enjoys problems of scale and complexity.

SkillsCast

Please log in to watch this conference skillscast.

Https s3.amazonaws.com prod.tracker2 resource 41088130 skillsmatter conference skillscast o9nohu

In a world of deep learning statistical techniques are out of fashion but can still be very effective tools. Twitter’s open source anomaly detection project uses a statistical technique call Seasonal Hybrid ESD.

In this talk you will go through the various steps in the algorithm from data preparation and time series decomposition through to finding potentially multiple anomalies. The technique could be used to spot deviations from behavioural patterns with the benefit that it is easy to see why an anomaly is unusual.

YOU MAY ALSO LIKE:

About the Speaker

Anomaly Detection: A breakdown of Twitter’s Seasonal Hybrid ESD

Peter Tillotson

Peter has been researching and solving leading-edge distributed computational problems for nearly 20 years. This began with intelligent agent systems; he tracked high-performance computing and their developments in both Grid and Cloud. More recently Peter has been closely following and working with Big Data, MapReduce, NoSQL and realtime streaming analysis. Peter is a Data Scientist, Trainer and Researcher who enjoys problems of scale and complexity.

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