Signal feasibility evaluates whether an observable contains stable, reproducible predictive structure before model development begins. A signal can look active and still be structurally unsuitable for reliable prediction.
Many signals contain movement, variation, or degradation-like behavior. The key question is whether that structure remains consistent enough across runs, regimes, or assets to support reliable prediction.
A signal may contain visible changes, patterns, or fluctuations without containing stable predictive information.
Prediction becomes more feasible when signal structure remains reproducible across comparable runs or conditions.
A signal should not only be predictable inside one dataset, but remain informative when deployed under new conditions.
For the detailed signal-feasibility page, the original FD001 and FD002 figures are used instead of simplified homepage visuals.
FD001 represents a more stable predictive regime. The signal structure remains sufficiently consistent to support predictive modeling and deployment-oriented interpretation.
FD002 contains visible signal activity, but the structure is less stable and less reproducible. This can lead to inferability collapse and unreliable deployment behavior.
A signal can be easy to forecast locally while still being non-informative for future system behavior. PFA separates prediction stability from structural consistency.
This figure highlights the difference between signals that are merely predictable and signals that contain reproducible, deployment-relevant predictive structure.
Signal feasibility is not always static. A signal may become more or less informative as degradation, regime transitions, or operating conditions evolve.
Rolling inferability helps detect whether predictive structure remains stable, improves, or collapses over time. This is important for identifying when a signal becomes unsafe for direct predictive deployment.
The same feasibility logic can be applied across industrial and scientific time-series domains, including turbofan degradation, vibration systems, battery degradation, and trajectory-based datasets.
The PFI map summarizes where signals fall across GO, LIMITED, and NO-GO regions. This supports early decisions about whether modeling should proceed, be constrained, or be stopped.
A focused PFA scan can determine whether your observable supports reliable predictive modeling before major model-development investment begins.