Data flows through layers of understanding. Each layer extracts more meaning. The final output: a trajectory prediction with a confidence cone showing where things are headed.
A snapshot classifier puts entities into fixed buckets. Trajectory scoring reveals direction. Drag the entities to see the difference.
Classifies into rigid buckets. Move an entity and the label changes instantly -- no memory.
Considers the full history. Direction arrows show where each entity is headed, not just where it is.
Drag any circle to reposition it. Notice how snapshot reclassifies instantly while trajectory remembers history.
Four foundational techniques, visualized. Interact with each to build intuition for how trajectory scoring extracts signal from time.
Drag the points to reshape the trend line. The R-squared value shows how well the line fits the data.
Inflection points are automatically detected and highlighted. These mark regime changes in the trajectory.
Noisy raw data becomes a smooth trend. Adjust the smoothing factor to control how much history matters.
The confidence cone widens over time. More data narrows it. Uncertainty is a feature, not a flaw.
Real-world data is noisy. Increase the noise level to see how single-signal approaches collapse while Trajectory Protocol's multi-signal approach stays accurate.
Trajectory scoring integrates at three points in your ML workflow. Choose one or use all three.
Enrich your feature vectors with trajectory signals: slope, acceleration, volatility, and momentum. Better features, better models.
Stream events into the trajectory engine. Get updated scores in milliseconds. Perfect for recommendation systems and anomaly detection.
Run trajectory extraction across your entire historical dataset. Discover patterns invisible to snapshot-based analysis.
Whether you are enriching features, scoring in real time, or mining historical patterns -- Trajectory Protocol is ready for your pipeline.
This product is one of 22 specialized solutions in the DobePros suite—a sentient infrastructure that evolves through real-world execution. By leveraging a unified, self-learning DNA for data, security, and commerce, every tool benefits from cross-platform hardening and collective technical resilience.