Recorded: April 28 | 1:00 p.m. ET
Outliers and anomalies refer to various types of occurrences in a time series. Spike of value, shift in level or volatility or a change in seasonal pattern are common examples. Anomaly detection depends on specific context.
In this month’s installment in our Data and Machine Learning Workshop Series, RiskSpan Co-Founder & CIO Suhrud Dagli is joined by Martin Kindler, a market risk practitioner who has spent decades dealing with outliers.
Suhrud and Martin explore unsupervised approaches for detecting anomalies.
Co-founder and CIO, RiskSpan
Managing Director, RiskSpan
