What matters
The problem comes before the model.
- Baselines and thresholds defined before evaluation.
- Drift controls and comparison across multiple signals or residuals.
- Metrics for false positives, detection delay and conditions where no conclusion should be drawn.
Lab Intelligence approach
Sources, assumptions, checks, versions, metrics and limitations remain visible. A more complex technique is used only when it adds value over an understandable baseline.
How the method works →Related workflows
Examples already documented.
These pages describe implementations or experiments from the laboratory; they do not imply that the same result transfers automatically to another context.
Process Sentinel
Detects anomalies and faults on a generic dynamic chemical process using a frozen 28-day synthetic data (simulated data) benchmark, isolated truth and a drift-aware PCA + process-residual ensemble.
Open project →WF-3Market Overview
Condenses cross-asset signals into regime, forecast, anomalies and a risk-on/risk-off view while keeping uncertainty visible.
Open project →WF-6Energy Crisis Thermometer
Combines energy, logistics and financial stress into a traceable 0–100 index with drivers, forecast and state machine.
Open project →Want to discuss a similar case?
Describe the problem and expected result in non-confidential terms. The first step is to identify the data, criteria and limitations that would make the case verifiable.