From Raw Signal to Prediction: What Actually Works (and What Doesn't)

Most predictive models fail — not because of poor modeling, but because the signal was never suitable for prediction in the first place. In many real-world systems, raw monitoring data appears complex and noisy, yet models are applied directly under the assumption that predictive structure is present. This often leads to unstable results, poor generalization, and wasted development effort. This work demonstrates, using real-world system data, how raw signals can be transformed into structured representations — and how this structure determines whether prediction is feasible at all. A step-by-step pipeline is presented:• raw multi-signal data (industry input)• structured signal representation• pre-instability detection and precursor analysis• final assessment of predictive feasibility The key result is a simple but critical insight:detecting structure is not sufficient — the structure must be consistent and reproducible to support prediction. This leads to a practical decision framework:• GO → signal supports prediction• LIMITED → unstable or context-dependent• NO-GO → signal does not support reliable prediction This introduces a necessary step before any predictive modeling effort: Can this signal support prediction at all? Skipping this step often results in models built on signals that were never predictive to begin with. The analysis is based entirely on real-world data (quantum calibration systems), with behavior consistent across industrial monitoring and battery degradation datasets. The objective is not to improve models, but to determine when modeling is meaningful.