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| Risk | Impact | Mitigation | |------|--------|------------| | – Real‑world patterns diverge from training data. | Forecast errors ↑ → false alarms. | Auto‑re‑train nightly with newest windows; monitor error drift via Prometheus. | | Latency spikes – Heavy what‑if recompute stalls UI. | Poor UX. | Cache recent model runs; fallback to a lightweight linear approximation when load > 80 %. | | Security – Remote command injection. | Device compromise. | Mutual TLS on all gRPC/MQTT channels; command signing with HMAC. | | Operator overload – Too many alerts. | Fatigue → ignored warnings. | Rate‑limit adaptive actions; aggregate into a single “Pulse Card” severity level. | | Hardware constraints – Edge device can’t receive frequent commands. | Unused feature. | Make the adaptive loop optional and configurable per device class. |

# ----- API endpoints ----- @app.post("/forecast") def get_forecast(payload: WhatIfRequest): pred = predict_next_45(payload.recent_windows, payload.hypothetical_delta) return "forecast": pred.tolist()

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