> For the complete documentation index, see [llms.txt](https://dashpay.gitbook.io/roboflux-whitepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://dashpay.gitbook.io/roboflux-whitepaper/4.-data-ingestion-and-preprocessing-layer/4.3-data-normalization-and-feature-engineering.md).

# 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.