Empirical validation of predictive feasibility using real telemetry signals.
This research program evaluates whether predictive feasibility can be determined before model development by analyzing intrinsic signal properties such as structural behavior, cross-run consistency, prediction stability, regime behavior, and robustness under alternative representations.
This validation program investigates whether prediction should begin with model development, or with a prior assessment of whether the signal itself supports meaningful prediction.
The central finding is that prediction stability alone is insufficient. A signal can appear locally predictable while still failing to provide reliable predictive information if cross-run consistency is absent.
The report evaluates this principle across telemetry signals, battery degradation data, clinical time-series data, quantum calibration signals, and turbofan degradation trajectories.
Can predictive feasibility be determined before model development using intrinsic signal properties?
The validation focuses on three key properties:
Evaluates consistency and prediction stability across real NASA SMAP/MSL telemetry signals.
Classifies whether predictive modeling is justified based on reproducible signal structure.
Tests whether stable predictive regions emerge under changing telemetry regimes.
Evaluates whether alternative signals or time scales recover predictive feasibility.
Tests whether missing consistency is caused by using linear correlation alone.
Tests whether segmentation, random grouping, or overlapping windows recover structure.
Evaluates whether smoothing, perturbation, or representation changes recover consistency.
Analyzes whether consistency follows system state progression or remains structurally absent.
Compares telemetry, battery, ICU, quantum, and turbofan data under one feasibility logic.
Defines the method from raw data to robustness validation and GO / LIMITED / NO-GO decision.
Translates feasibility results into practical pre-model deployment decisions.
Evaluates whether consistency peaks are caused by persistent structure or temporary alignment.
This validation program shows that predictive modeling should not begin with model selection. It should begin with an explicit assessment of whether the signal contains reproducible predictive structure.
For industrial applications, this helps prevent investment in models that may appear accurate locally but fail because the underlying signal does not support stable, generalizable prediction.
Many signals appear structured and locally predictable, yet fail when deployed across changing conditions, independent runs, or operational regimes.
The program demonstrates that cross-run consistency is a necessary condition for meaningful prediction. Without reproducible structure, prediction accuracy can be misleading.
This supports a practical decision principle: before building predictive AI, first determine whether prediction is structurally justified.
Prediction failure is not always caused by poor models.
In many cases, the signal itself does not contain stable, reproducible structure across runs.
A pre-model Predictive Feasibility Assessment can identify whether a system is GO, LIMITED, or NO-GO before large-scale model development begins.