Public experimental prototype - local data, indicative reading

INSACERMO Signal Diagnostic V2.1

Multi-column state reading: load a dynamic CSV and observe how several variables change together around a possible shift zone. AI mode faithfully follows the INSACERMO-U/R pack for train_loss / val_loss. Sensor, series and table modes remain experimental.

Everything stays local in the browser Canonical AI mode if train_loss / val_loss Experimental multi-column

How to read results

The tool looks for a zone where data behavior changes. The detected zone tells you where to look.

The before/after table shows what changes: mean, variance, slope, volatility. Contributions show which columns move most around the zone.

Xi/TCI metrics are displayed only in AI train_loss / val_loss mode. For sensors, series and tables, the reading remains experimental.

Short glossary

Detected zone : place where behavior seems to change.

Contribution : column that moves most around the zone.

Change score : indicative intensity of change.

Examples : some are public/classic, others are synthetic or educational.

Data

Load or paste a CSV. The engine automatically chooses the mode: canonical AI if train / validation losses exist, otherwise experimental multi-column reading.

Built-in examples: classic public series, synthetic examples and educational edge cases. Hover over a button to read what it is.

Selected state columns

State reading

No analysis launched.
-Verdict
-Detected zone
-Columns used
-Indicative confidence
Score reading

Displayed metrics

Run an analysis to display the main result.

Official metrics are displayed only in canonical AI mode.

Change score : indicative intensity of before/after change. The higher it is, the more the zone deserves examination. This score remains educational.

MetricValue / statusReading
What changes around the zone

Multivariate before / after

Mean = general level. Variance = dispersion. Slope = trend. Volatility = local agitation.

If the mean changes, the signal level shifts. If variance or volatility increases, the regime becomes less stable. If the slope changes, the trend reverses or accelerates.

MeasureBeforeAfterDelta

Column contributions

In experimental mode, contribution = relative size of before/after change per normalized column. It helps locate what moves, without certified diagnosis.

Simple null models

Local comparison with 40 shuffled versions of the state columns. Educational control, not definitive statistical proof.

-Not run

Displayed limits

AI mode is faithful to the pack for train_loss / val_loss logs. Multi-column mode is an experimental reading inspired by INSACERMO-U: it is not yet the canonical Xi/TCI protocol from the reproducible pack. Exploration tool only: no certified medical, industrial or scientific diagnosis, no automatic decision. Data remains local in the browser. Do not use it to target, exploit, disrupt or profile systems or people without authorization.