Everyone seems eager to incorporate ML in their anomaly detection solutions, but it doesn’t always make sense to use ML. Using statistical methods to detect one-off peaks in time series data is extremely effective and efficient. However, statistical methods fail when trying to detect contextual or collective anomalies.
If you want to alert on these anomalies, you probably need to use some type of ML. Anais Dotis-Georgiou outlines three statistical methods, introduces two popular ML algorithms, and describes the relationship between contextual and collective anomalies. Join in to learn how easy it is to use k-means to alert on a contextual anomaly in EKG data.
Anais Dotis-Georgiou is a developer advocate at InfluxData with a passion for making data beautiful using data analytics, AI, and machine learning. She takes the data that she collects and does a mix of research, exploration, and engineering to translate the data into something of function, value, and beauty. When she’s not behind a screen, you can find her outside drawing, stretching, or chasing after a soccer ball.
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