Anomaly Detection in Time Series
summary
Covers methods for detecting anomalies in time series data as dimensions and data rates grow. Discusses algorithmic approaches and practical techniques for spotting unusual activity in high-volume, high-dimensional temporal datasets used in monitoring and analytics.
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chapters (10)
- 0:00Introduction
- 3:13Business needs
- 5:20Production process
- 10:57Dataset overview
- 14:28Model goals and metrics
- 19:01Autoencoder principles
- 22:36Time series examples
- 25:05Autoencoder architectures
- 33:29Results and model selection
- 34:11Q&A
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