diff --git a/src/lib/analytics/mood-prognosis/__tests__/forecast.test.ts b/src/lib/analytics/mood-prognosis/__tests__/forecast.test.ts
new file mode 100644
index 000000000..78c7df9f6
--- /dev/null
+++ b/src/lib/analytics/mood-prognosis/__tests__/forecast.test.ts
@@ -0,0 +1,71 @@
+/**
+ * `computeForecast` — the two floors a fit has to clear, and the refusals.
+ *
+ * The first floor is the ladder, on rated days. The second is the one this
+ * file exists for: a complete-case matrix can shrink well below the rated-day
+ * count, because a day missing one of the fit's inputs is dropped rather than
+ * imputed. An account with thirty rated days but a feature recorded on only
+ * two thirds of them fits on twenty complete rows, and a forecast "built from
+ * twenty days" past a floor that was supposed to mean thirty is a floor that
+ * does not hold. The fit is refused below `MIN_ENTRIES_FOR_FORECAST` complete
+ * rows, not only below thirty rated days.
+ */
+import { describe, expect, it } from "vitest";
+
+import { computeForecast } from "../forecast";
+import type { PrognosisDayInput } from "../features";
+import { MIN_ENTRIES_FOR_FORECAST } from "../thresholds";
+
+const EMPTY_LINKED = {
+  sleepAsleep: null,
+  steps: null,
+  activeEnergy: null,
+  restingHeartRate: null,
+  heartRateVariability: null,
+};
+
+function dayKey(index: number): string {
+  return new Date(Date.UTC(2026, 0, 1 + index)).toISOString().slice(0, 10);
+}
+
+/**
+ * `rated` days carrying a rating driven by A2, of which the most recent
+ * `withFeature` actually carry the A2 slider. The rest are older face-only
+ * days: rated, but with no input the fit can use, so they drop out of the
+ * complete-case matrix. The feature sits on the RECENT days on purpose, so the
+ * days the write window would score are scorable — otherwise the refusal is
+ * "no recent day to score" rather than the "too few complete rows" this file
+ * is about.
+ */
+function sparseDays(rated: number, withFeature: number): PrognosisDayInput[] {
+  return Array.from({ length: rated }, (_, i) => {
+    const stress = i % 11;
+    const hasFeature = i >= rated - withFeature;
+    return {
+      day: dayKey(i),
+      a1: Math.round(0.8 * (10 - stress) + 1),
+      dimensions: hasFeature ? { a2: stress } : {},
+      context: null,
+      linked: { ...EMPTY_LINKED },
+    };
+  });
+}
+
+describe("computeForecast complete-row floor", () => {
+  it("refuses a fit that clears the rated-day floor but not the complete-row one", () => {
+    // 40 rated days, A2 on 25 of them: coverage 62.5% clears the candidate
+    // floor, so A2 is screened in, but the complete-case matrix is 25 rows —
+    // under the 30-row floor.
+    const result = computeForecast(sparseDays(40, 25));
+    expect(result.status, JSON.stringify(result)).toBe("refused");
+  });
+
+  it("writes once the complete rows themselves reach the floor", () => {
+    // Every rated day carries the feature, so complete rows equal rated days.
+    const result = computeForecast(sparseDays(40, 40));
+    expect(result.status).toBe("written");
+    if (result.status === "written") {
+      expect(result.n).toBeGreaterThanOrEqual(MIN_ENTRIES_FOR_FORECAST);
+    }
+  });
+});
diff --git a/src/lib/analytics/mood-prognosis/forecast.ts b/src/lib/analytics/mood-prognosis/forecast.ts
index e5cc768b1..5a9ec91e4 100644
--- a/src/lib/analytics/mood-prognosis/forecast.ts
+++ b/src/lib/analytics/mood-prognosis/forecast.ts
@@ -23,7 +23,7 @@ import {
 } from "./features";
 import { PROGNOSIS_WRITE_WINDOW_DAYS } from "./load";
 import { featureContributions, predictWithFit } from "./ridge";
-import { forecastGate } from "./thresholds";
+import { MIN_ENTRIES_FOR_FORECAST, forecastGate } from "./thresholds";
 import { fitPrognosisModel } from "./validate";
 
 /** The lowest and highest value the target scale can express. */
@@ -101,6 +101,18 @@ export function computeForecast(
   const model = matrix === null ? null : fitPrognosisModel(matrix);
   if (model === null) return { status: "refused", reason: "no-pattern" };
 
+  // The second floor, and the one the rated-day gate above cannot see. A day
+  // missing one of the selected features is dropped rather than imputed, so a
+  // sparse-context account can clear thirty rated days and still fit on a
+  // handful of complete rows. `n` shown beside the value and a wide band on a
+  // small fit keep it from being dishonest, but a forecast "built from twelve
+  // days" past a floor that was supposed to mean thirty is a floor that does
+  // not hold. The number of days the fit ACTUALLY used has to clear the same
+  // threshold, not only the number of days that carried a rating.
+  if (model.n < MIN_ENTRIES_FOR_FORECAST) {
+    return { status: "refused", reason: "no-pattern" };
+  }
+
   const rows: ForecastRow[] = [];
   for (const day of rated.slice(-writeWindowDays)) {
     const standardised = standardiseDay(day, model.standardisation);