Machine Learning and Statistical Methods for Time Series Analysis
Submitted by Aravind Putrevu (@aravindputrevu) on Monday, 16 April 2018
In this talk, I will present a deep algorithmic dive into the new machine learning technologies available in the Elastic Stack and how they can be applied to real datasets.
Specifically, I will focus on some of the unsupervised machine learning techniques Elastic uses, and the challenges and constraints which exist in order to provide operationally useful insight when applying these technologies to real time series data.
- Machine Learning Overview
- Time Series Anomaly Detection
Aravind is a loquacious person, who has something to talk about everything. He is passionate about evangelising technology, meeting developers and helping in solving their problems. He is a backend developer and has six years of backend development experience.
Previously, He worked at McAfee Antivirus as a Sr. Software Engineer in Cloud Security Domain. He has profound interest in Search, ML, Security and IoT tech. In his free time, he plays around with his pet RasPi or Arduino.