refactor: extract 6 prediction signals into Signal classes

The 803-line NationalFuelPredictionService had six private compute*Signal
methods, a private linearRegression helper, and a private disabledSignal
shape factory all crammed together. Each signal is now an independently
testable class.

- App\Services\Prediction\Signals\Signal — interface
- App\Services\Prediction\Signals\SignalContext — input value object
  (FuelType + optional lat/lng + hasCoordinates() helper)
- App\Services\Prediction\Signals\AbstractSignal — shared
  disabledSignal() and linearRegression() helpers
- TrendSignal, DayOfWeekSignal, BrandBehaviourSignal, StickinessSignal,
  RegionalMomentumSignal, OilSignal — one class each, extending
  AbstractSignal

NationalFuelPredictionService receives the 6 signal classes via constructor
injection and orchestrates them. The lat/lng null-guard for regional
momentum now lives inside RegionalMomentumSignal::compute() so the
coordinator no longer branches on coordinate presence.

Aggregation, weekly summary, and reasoning helpers stay in the service
for now — they are coupled to the public predict() output shape and are
candidates for a follow-up extraction once a stable API is locked in.

Service: 803 → 414 lines.

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

app/Services/Prediction/Signals/AbstractSignal.php

@@ -0,0 +1,61 @@
+<?php
+
+namespace App\Services\Prediction\Signals;
+
+abstract class AbstractSignal implements Signal
+{
+    /** @return array{score: 0.0, confidence: 0.0, direction: 'stable', detail: string, data_points: 0, enabled: false} */
+    protected function disabledSignal(string $detail): array
+    {
+        return [
+            'score' => 0.0,
+            'confidence' => 0.0,
+            'direction' => 'stable',
+            'detail' => $detail,
+            'data_points' => 0,
+            'enabled' => false,
+        ];
+    }
+
+    /**
+     * Least-squares linear regression. x = array index, y = value.
+     *
+     * @param  float[]  $values
+     * @return array{slope: float, r_squared: float}
+     */
+    protected function linearRegression(array $values): array
+    {
+        $n = count($values);
+
+        if ($n < 2) {
+            return ['slope' => 0.0, 'r_squared' => 0.0];
+        }
+
+        $xMean = ($n - 1) / 2.0;
+        $yMean = array_sum($values) / $n;
+
+        $numerator = 0.0;
+        $denominator = 0.0;
+
+        foreach ($values as $i => $y) {
+            $x = $i - $xMean;
+            $numerator += $x * ($y - $yMean);
+            $denominator += $x * $x;
+        }
+
+        $slope = $denominator > 0.0 ? $numerator / $denominator : 0.0;
+
+        $ssRes = 0.0;
+        $ssTot = 0.0;
+
+        foreach ($values as $i => $y) {
+            $predicted = $yMean + $slope * ($i - $xMean);
+            $ssRes += ($y - $predicted) ** 2;
+            $ssTot += ($y - $yMean) ** 2;
+        }
+
+        $rSquared = $ssTot > 0.0 ? max(0.0, 1.0 - ($ssRes / $ssTot)) : 0.0;
+
+        return ['slope' => $slope, 'r_squared' => $rSquared];
+    }
+}