Nsfs-338-rm-javhd.today01-45-23 Min | TOP ✯ |

Below is a minimal Python sketch of the forecast service (using prophet for seasonality and a LightGBM booster for residuals). It’s ready to be wrapped in FastAPI.

# forecast_service.py
import pandas as pd
from prophet import Prophet
import lightgbm as lgb
from fastapi import FastAPI, Query
from pydantic import BaseModel
import uvicorn
app = FastAPI(title="Live‑Pulse Adaptive Forecast")
# ----- Load pre‑trained artefacts (once at startup) -----
prophet_model = Prophet(yearly_seasonality=False, daily_seasonality=True)
prophet_model.load("models/prophet.pkl")
lgb_model = lgb.Booster(model_file="models/lgb_residual.txt")
# ----- Input schema -----
class WhatIfRequest(BaseModel):
    recent_windows: list[float]        # last 45 minute‑averages
    hypothetical_delta: float = 0.0    # e.g., +10% buffer size
# ----- Core forecasting function -----
def predict_next_45(recent, delta=0.0):
    # 1️⃣ Build DataFrame for Prophet
    df = pd.DataFrame(
        "ds": pd.date_range(end=pd.Timestamp.utcnow(), periods=45, freq="1T"),
        "y": recent
    )
    future = prophet_model.make_future_dataframe(periods=45, freq="1T")
    prophet_forecast = prophet_model.predict(future)["yhat"].iloc[-45:].values
# 2️⃣ LightGBM residual correction
    #    Features: recent windows + delta (broadcast)
    X = pd.DataFrame(
        f"lag_i": recent[-i] for i in range(1, 6)  # 5‑lag features
    , index=[0])
    X["delta"] = delta
    residuals = lgb_model.predict(X)[0] * np.ones(45)
# 3️⃣ Combine
    return prophet_forecast + residuals
# ----- API endpoints -----
@app.post("/forecast")
def get_forecast(payload: WhatIfRequest):
    pred = predict_next_45(payload.recent_windows, payload.hypothetical_delta)
    return "forecast": pred.tolist()
@app.get("/health")
def health_check():
    return "status": "ok"
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

Hook this service into the streaming layer, and you already have a live‑pulse endpoint that can be called every minute or on-demand for a “what‑if” simulation.

| Feature | Live‑Pulse Adaptive Forecast (LPAF) | |-------------|--------------------------------------| | What | Minute‑resolution 45‑minute rolling forecast + auto‑tuning + interactive “what‑if” sandbox. | | Why | Turns reactive monitoring into proactive, self‑optimizing operation. | | How | Edge → MQTT → 1‑min windows (Flink) → Hybrid Prophet/LightGBM model → Adaptive controller → UI Pulse Card + What‑If slider. | | Key Benefits | • Anticipate issues 45 min ahead
• Reduce manual tuning
• Instantly evaluate configuration changes
• Consolidated, colour‑coded health badge | | Target Metrics | ≤ 4 % forecast MAE, ≤ 150 ms adaptation latency nsfs-338-rm-javhd.today01-45-23 Min

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| Problem | Current Gap | LPAF Solution | |---------|--------------|----------------| | Blind spots – Operators can only see the past or a static forecast that quickly becomes stale. | No minute‑level forward view; decisions are reactive. | Continuous 45‑minute rolling forecast refreshed every 1 minute. | | Manual tuning – Users must adjust thresholds (e.g., temperature, bandwidth) by trial‑and‑error. | Hard‑coded rules; no learning from history. | Adaptive algorithms auto‑tune parameters based on live data trends. | | What‑if uncertainty – “What if I change X now?” is impossible to answer instantly. | No simulation sandbox. | Interactive “What‑If Slider” that instantly recomputes the forecast for any proposed change. | | Data overload – Raw logs are massive and unstructured. | Operators drown in raw numbers. | Summarized, colour‑coded “Pulse Card” that tells you “Green = stable, Yellow = watch, Red = intervene”. |