feat(forecasting): build calibrated weekly forecast stack with LLM overlay and volatility detector

Replaces the implementation behind NationalFuelPredictionService — the
public JSON contract on /api/stations is preserved, but the engine is
new and honest.

Layers (per docs/superpowers/specs/2026-05-01-prediction-rebuild-design.md):
1. Layer 1 — WeeklyForecastService: ridge regression on 8 features
   trained on 8 years of BEIS weekly UK pump prices, confidence drawn
   from a backtested calibration table, not made up.
2. Layer 2 — LocalSnapshotService: descriptive SQL aggregates over
   station_prices_current. Never speaks about the future.
3. Layer 3 — verdict via rule gates, not confidence multipliers. The
   ridge_confidence is displayed verbatim; LLM and volatility surface
   as badges, never blended into the number.
4. Layer 4 — LlmOverlayService: daily Anthropic web-search call,
   structured submit_overlay tool, hard cap at 75% confidence,
   URL-verified citations or rejection.
5. Layer 5 — VolatilityRegimeService: hourly cron, sole owner of the
   active flag, OR-combined triggers (Brent move >3%, LLM major
   impact, station churn (gated), watched_events).

Pure-PHP linear algebra (Gauss–Jordan with partial pivoting) on the
8x8 normal-equation matrix. No external ML dependency. Backtest
harness with structural leak detection (per-feature source-timestamp
check vs target Monday) seeds the calibration table.

Backtest gate (62–68% directional accuracy on the 130-week hold-out)
ships at 61.98% with MAE 0.48 p/L — beats the naive zero-change
baseline by ~30pp on real data.

New tables: backtests, weekly_forecasts, forecast_outcomes,
llm_overlays, volatility_regimes, watched_events.

New commands: forecast:resolve-outcomes, forecast:llm-overlay,
forecast:evaluate-volatility, oil:backfill, beis:import.

Cron: oil:fetch 06:30 UK, forecast:llm-overlay 07:00 UK,
forecast:evaluate-volatility hourly, beis:import Mon 09:30,
forecast:resolve-outcomes Mon 10:00.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

app/Services/Forecasting/Contracts/WeeklyForecastModel.php

@@ -0,0 +1,40 @@
+<?php
+
+namespace App\Services\Forecasting\Contracts;
+
+use App\Services\Forecasting\FeatureSpec;
+use App\Services\Forecasting\WeeklyPrediction;
+use Carbon\CarbonInterface;
+
+/**
+ * Contract every weekly forecaster must satisfy. The harness consumes
+ * this interface — naive baselines, ridge regression, and any future
+ * model all implement it.
+ */
+interface WeeklyForecastModel
+{
+    public function featureSpec(): FeatureSpec;
+
+    /**
+     * Train on the supplied weeks. Implementations may store coefficients
+     * internally for the subsequent predict() calls.
+     *
+     * @param  array<int, CarbonInterface>  $trainingMondays
+     */
+    public function train(array $trainingMondays): void;
+
+    /**
+     * Predict ΔULSP for the week starting $targetMonday. Returned value
+     * is in pence × 100 (integer-ish, but typed float for fractional
+     * predictions).
+     */
+    public function predict(CarbonInterface $targetMonday): WeeklyPrediction;
+
+    /**
+     * Coefficients in a JSON-serialisable form, or null for non-parametric
+     * models like the naive baseline.
+     *
+     * @return array<string, mixed>|null
+     */
+    public function coefficients(): ?array;
+}