Signal Aggregation for Trends

A trend is not a single data point. It is a pattern that emerges from the convergence of multiple independent signals across different source categories. AI hiring surges across five competitors is a signal cluster. That same signal cluster, combined with increasing AI-related patent filings, growing community engagement around AI topics, and analyst language shifting toward AI-first positioning, constitutes a trend. Signal aggregation is the systematic process of collecting these signals from different categories and detecting when they converge toward the same directional conclusion.

1. Multi-Category Signal Collection Monitor signals across all seven OSINT categories for each tracked theme. A hiring signal alone is ambiguous. A hiring signal + patent signal + earnings language signal + community signal is a converging trend. The more categories that contribute signals, the higher the trend confidence.
2. Convergence Scoring Assign a convergence score based on the number of independent signal categories pointing in the same direction. Two categories = emerging (monitor). Three categories = forming (analyze). Four or more = established (brief and recommend). The convergence score is the primary confidence measure for trend assessment.
3. Velocity Measurement Track how quickly signals accumulate. A trend that moves from two-category to four-category convergence in one quarter is accelerating — the market is moving fast. A trend that stays at two categories for three quarters is stalling or false. Velocity determines urgency.