Automated Trend Monitoring

Automated trend monitoring operates on three levels. Level 1: known trend tracking — monitoring the lifecycle progression of trends you have already identified. Is the AI-first trend accelerating or stalling? Level 2: theme emergence detection — scanning for new signal clusters that do not match any known trend. A new cluster of signals around a topic that was not previously tracked is a candidate for a new trend. Level 3: anomaly detection — flagging signal patterns that deviate significantly from historical norms without a clear explanation. An anomaly may be a false alarm or the earliest signal of a trend that has no name yet.

1. Level 1: Known Trend Dashboard For each tracked trend, display current lifecycle stage, convergence score, signal velocity, and recent signals. Alert when a trend advances to a new lifecycle stage or when velocity changes significantly. This is the standard monitoring view.
2. Level 2: Theme Emergence Scanner AI-assisted clustering of new signals that do not map to known trends. When a cluster exceeds the minimum threshold (3+ signals in 2+ categories within 30 days), surface it as a candidate trend for analyst evaluation. Most candidates will be noise. The few that are real are high-value early detections.
3. Level 3: Anomaly Alerts Statistical deviation detection across all signal categories. When a metric — hiring volume, patent filings, community activity — deviates more than 2 standard deviations from its rolling average, flag it. Anomalies precede trends, but most anomalies are not trends. The analyst determines which is which.