This content originally appeared on DEV Community and was authored by Bharath Prasad
Ever received a bank alert asking, “Did you just make this transaction?” That’s anomaly detection in action. In machine learning, anomaly detection is all about identifying unusual data points—ones that don’t follow the expected pattern.
Whether it’s a spike in CPU usage, a security breach, or a drop in app performance, anomalies often signal deeper issues. And in today’s data-rich world, catching these early is crucial for both performance and security.
There are three main approaches:
Supervised Learning: Requires labelled data, including known anomalies.
Unsupervised Learning: Assumes most data is normal; detects the rest.
Semi-supervised Learning: Trains only on normal data, flags anything unusual.
Popular ML algorithms for anomaly detection include:
Isolation Forest
One-Class SVM
Autoencoders
LSTM (especially for time series data)
Time series anomaly detection is essential for monitoring systems, finance, and IoT—basically any scenario where data evolves over time.
Real-world use cases? Fraud detection, predictive maintenance, patient monitoring, system performance tracking—you name it.
Still, challenges like lack of labelled anomalies, false positives, and constantly evolving data can complicate things. That’s why hands-on experience is key.
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This content originally appeared on DEV Community and was authored by Bharath Prasad