Signal Audit

What problem does it solve?

Systematically quantify the predictive information content of candidate signals to guide feature selection before model building, reducing wasted effort on low-value features.

Core Features & Use Cases

• Mutual information estimation for continuous signals and targets
• Regression normalization and regime-specific MI analysis
• Lag optimization to identify leading indicators and capture temporal effects
• Comprehensive signal audit reporting with actionable insights
• Signal decay tracking to monitor value over time
• Use Case: Prioritize top signals for a new predictive model and condition features by regime

Quick Start

Run a signal audit on your dataset to identify top signals by mutual information and review regime-specific results before building a model