Home▸AI & Machine Learning▸Detecting Anomalies in Conflict-Event Time Series with Isolation Forest

🌲 Detecting Anomalies in Conflict-Event Time Series with Isolation Forest

An interactive 3D Isolation Forest anomaly detector running on a synthetic conflict-event time series, flagging statistically unusual spikes for review.

AI & Machine Learning3DAdvanced60 FPS

A synthetic daily conflict-event log is rendered as a 3D bar chart, and a real, simplified Isolation Forest runs in the browser to score every day for anomaly, flagging the statistically unusual spikes for review.

🔬 What It Demonstrates

Isolation Forest repeatedly partitions the data with random splits; points that separate from the rest in very few splits get short average path lengths and high anomaly scores — exactly what is animated by the translucent split planes.

🎮 How to Use

Adjust contamination to control how many top-scoring days get flagged, change tree count to see the score stabilize, and inject spikes to watch the model react. Toggle the split-plane animation and regenerate the series any time.

💡 Did You Know?

Because Isolation Forest needs no labelled anomalies to train on, it's a common first pass in open-data pipelines like ACLED or GDELT before analysts manually review flagged events.

⚙ Under the hood

An interactive 3D Isolation Forest anomaly detector running on a synthetic conflict-event time series, flagging statistically unusual spikes for review.

anomaly detectionisolation foresttime series analysismachine learningdata miningstatistical modelingThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install