4.3 Data Normalization & Feature Engineering

Prior to AI inference, all data undergoes a rigorous normalization and transformation protocol:

  • Zero-Center Scaling: Converts numeric telemetry to zero-mean, unit-variance for numerical stability in gradient-based learning systems.

  • Temporal Windowing: Segments time-series data into overlapping sliding windows to capture temporal correlations.

  • Derived Feature Construction: Generates secondary metrics (e.g., moving averages, variance trends, spectral entropy) to enhance anomaly detection sensitivity.

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