Forecast improvements

2026-06-11 14:55:33 -04:00
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 # Forecast Accuracy Fix Plan
 **Written:** 2026-06-10, from a code + live-data review of the forecasting pipeline.
 **Goal:** eliminate the systematic ~1.7–2x over-forecast bias, recover demand the model currently ignores, and fix the accuracy measurement so improvements are visible and long-lead forecasts are validated.
 Read this whole document before starting. Fixes are grouped into phases; each phase is independently deployable and has its own validation step. Line numbers are as of 2026-06-10 — re-locate by function name if the file has drifted.
 ---
 ## 1. Diagnosis summary (measured 2026-06-10)
 The dashboard headline is **202% WMAPE**. Decomposition of that number, all measured against `forecast_accuracy` run 129 and ad-hoc queries:
 | Finding | Evidence |
 |---|---|
 | Daily-grain WMAPE has a ~190% *floor* for this catalog | Avg demand ≈ 0.11 units/product/day. A perfect rate forecast of intermittent demand scores ≈ 2e^−λ ≈ 190%. A trivial trailing-30d-average naive forecast scores **204%** on the same products/days; the engine scores 221% (slightly *worse than naive*). |
 | Same forecasts at 21-day-per-product grain: **109%**; bias-corrected: **75%** | Half the headline is metric grain, most of the rest is bias. |
 | Aggregate over-forecast **+70%** (227,690 forecast vs 133,861 actual units) | Portfolio daily ratio is 1.5–2.5x on most days. |
 | Decay phase 2.47x over (fc 51,675 / act 20,915) | Root cause F1: velocity inflated **4.07x** (measured: 1.353 vs true 0.332 units/day) by averaging over sparse snapshot rows. |
 | Preorder phase 2.15x over (fc 67,212 / act 31,189) | Root cause F4: launch curve applied at age=0 starting *today*, ignoring that the product hasn't arrived. |
 | Mature phase 1.69x over (fc 57,857 / act 34,313) | Root causes F2 (history edge truncation) + F3 (seasonal double-count). |
 | Dormant products sold **16,180 units** (~11% of demand) against zero forecasts | Root cause F5; also excluded from the headline metric, so invisible. |
 | All 879,800 accuracy samples are in the **1–7d lead bucket** | Root cause F7: archiving design only ever saves yesterday's slice. 30–90d forecasts (what purchasing uses) are never validated. |
 | Launch phase is healthy: WMAPE 100%, bias −6%, beats naive | The lifecycle-curve concept works; its calibration inputs are broken. Don't redesign it. |
 **Key data fact** underlying several fixes: `daily_product_snapshots` is **activity-based and sparse** — only ~500–1,800 of ~38K products have a row on a given day. Verified: every pid-day with an order DOES have a snapshot row and units match (5,234/5,234 pid-days, 8,980 vs 8,984 units over 7 days). So *missing row = zero sales*, and any query that aggregates over only the rows that exist is averaging over sold-days.
 ---
 ## 2. Environment & operational notes
 - **Files:** engine is `inventory-server/scripts/forecast/forecast_engine.py`; orchestrator `run_forecast.js` in the same dir; consumer endpoints in `inventory-server/src/routes/dashboard.js` (`/forecast/metrics` ~line 308, `/forecast/accuracy` ~line 647); overview UI in `inventory/src/components/overview/ForecastMetrics.tsx` and `ForecastAccuracy.tsx`.
 - **Local `inventory-server/` is NFS-mounted to `/var/www/inventory/` on the netcup server.** Edits made locally appear on the server immediately — no copy step. Do NOT run bulk `grep`/`find`/`node --check` over `inventory-server/` locally (the mount hangs); `ssh netcup` and run them there.
 - **Avoid the glob tool** for search in this repo; use bash (`grep`/`rg` via ssh for server-side trees).
 - **Scheduling:** the engine runs daily at **09:30:01 server time** (runs table is conclusive), but the cron entry is NOT in matt's crontab, `/etc/cron.d`, or pm2. Likely root's crontab (`sudo crontab -l` to confirm). You do not need to touch the schedule for these fixes; just know a run fires at 09:30 daily and occasionally skips days (e.g. 2026-06-07/08).
 - **Manual test runs:** `ssh netcup`, then `cd /var/www/inventory/scripts/forecast && node run_forecast.js`. Takes ~3.5–4 min. Safe to run any time: the engine TRUNCATEs and rebuilds `product_forecasts`, archives prior past-dated rows, and records a new `forecast_runs` row. Python deps live in the server venv (`venv/`); `run_forecast.js` handles env + venv automatically.
 - **DB access for validation:** `ssh netcup`, then `PGPASSWORD=6D3GUkxuFgi2UghwgnUd psql -h localhost -U inventory_readonly -d inventory_db`. The engine itself connects with the write user via env vars loaded from `/var/www/inventory/.env` — schema changes should be made idempotently *inside the engine code* (the file already uses `CREATE TABLE IF NOT EXISTS` / `CREATE INDEX IF NOT EXISTS`; use `ALTER TABLE ... ADD COLUMN IF NOT EXISTS` the same way) so no manual migration is needed.
 - **Python gotchas already handled in this file (don't regress):** numpy types must go through the registered psycopg2 adapters; `pd.Series.combine_first()` keeps zeros over real data — use `reindex(..., fill_value=0.0)`.
 - Engine runtime budget: currently ~212–227s. Phases 1–2 shouldn't move it meaningfully; Phase 3's extra archiving adds one INSERT…SELECT. If runtime balloons past ~6 min, investigate before shipping.
 - `--backfill` mode (`backfill_accuracy_data`) is an in-sample backtest using the *old* formulas. **Do not run it anymore**; there is enough real out-of-sample history. Updating it to match the new logic is optional/low priority (F11).
 ---
 ## Phase 1 — Bias bugs in the engine (no schema changes)
 ### F1. Decay velocity: stop averaging over sparse snapshot rows
 **Where:** `forecast_engine.py`, `batch_load_product_data()`, the decay query (~lines 697–710).
 **Problem:** `AVG(COALESCE(dps.units_sold, 0))` runs over only the snapshot rows that exist — mostly sold-days. Measured inflation on the current 975 decay products: **4.07x** (1.353 vs 0.332 true units/day). This feeds `compute_scale_factor()` for the decay phase and is the single largest bias source.
 **Fix:** divide the sum by calendar days in the window, clipped to the product's age (decay products are 14–60 days old, so a 20-day-old product's window is 20 days, not 30):
 ```sql
 SELECT dps.pid,
    SUM(COALESCE(dps.units_sold, 0))::float
      / GREATEST(LEAST(30, (CURRENT_DATE - pm.date_first_received::date)), 1) AS avg_daily
 FROM daily_product_snapshots dps
 JOIN product_metrics pm ON pm.pid = dps.pid
 WHERE dps.pid = ANY(%s)
  AND dps.snapshot_date >= CURRENT_DATE - INTERVAL '30 days'
  AND dps.snapshot_date >= pm.date_first_received::date
 GROUP BY dps.pid, pm.date_first_received
 ```
 No Python-side changes needed; `data['decay_velocity']` keeps the same shape. Products with zero snapshot rows in the window still get no entry → existing `scale = 1.0` fallback applies (acceptable: decay classification requires `sales_velocity_daily > 0`, so truly dead products don't reach this path).
 ### F2. Mature history: reindex over the full calendar window
 **Where:** `forecast_engine.py`, `forecast_mature()` (~lines 833–836).
 **Problem:** `hist.set_index('snapshot_date').resample('D').sum()` only spans first-snapshot → last-snapshot. Interior gaps correctly become zeros, but **leading and trailing quiet periods are absent**, so the Holt level is fitted on the product's busy span. A marginal mature product whose activity clusters in 2 of the last 8 weeks gets a level ~4x too high.
 **Fix:** replace the resample with an explicit reindex over the full `EXP_SMOOTHING_WINDOW` ending yesterday:
 ```python
 hist = history_df.copy()
 hist['snapshot_date'] = pd.to_datetime(hist['snapshot_date'])
 hist = hist.set_index('snapshot_date')['units_sold']
 full_index = pd.date_range(
    end=pd.Timestamp(date.today() - timedelta(days=1)),
    periods=EXP_SMOOTHING_WINDOW, freq='D')
 series = hist.reindex(full_index, fill_value=0.0).values.astype(float)
 ```
 Notes: (pid, snapshot_date) is unique in `daily_product_snapshots`, so no duplicate-index risk. `observed_mean` and the `cap` recompute over the full window automatically (intended — the cap gets correspondingly tighter). Mature products are by definition >60 days old, so the 60-day window never predates first receipt. Do NOT use `combine_first` (see gotchas above).
 ### F3. Stop double-applying the monthly seasonal index
 **Where:** `forecast_engine.py`, `generate_all_forecasts()` — the `seasonal_multipliers` pre-compute (~lines 959–961) and application (~line 1050).
 **Problem:** every per-product calibration (decay velocity, mature Holt level, launch first-week scale, preorder rate, slow-mover velocity) is fitted on *raw recent actuals*, which already embed the current month's seasonal level. The forecast then multiplies by the **absolute** monthly index of the target date. Example from the live indices (`forecast_runs.phase_counts` for run 129): May = 1.224 (sale month), June = 0.982. Early-June forecasts were calibrated on May-sale-inflated velocities and barely discounted — a structural ~25% over-forecast at that transition, and it'll be worse around November (1.316).
 **Fix:** apply the seasonal index *relative to the calibration period*. Compute a calibration index as the average monthly index over the trailing 30 calendar days (robust at month boundaries), then divide:
 ```python
 today = date.today()
 trailing = [today - timedelta(days=i) for i in range(1, 31)]
 calibration_index = float(np.mean([monthly_indices.get(d.month, 1.0) for d in trailing]))
 seasonal_multipliers = [
    monthly_indices.get(d.month, 1.0) / max(calibration_index, 0.1)
    for d in forecast_dates
 ]
 ```
 Leave the DOW multipliers absolute — every calibration is a multi-week average and therefore DOW-neutral, so reshaping by absolute DOW indices is correct.
 **Optional sub-fix (same area, low priority):** the monthly indices are computed from a single trailing 365-day window, so each month appears once and YoY growth contaminates "seasonality". A cheap improvement is widening `SEASONAL_LOOKBACK_DAYS` to 730 and averaging the two observations of each month. Do this only after the main fixes are validated.
 ### Phase 1 validation
 Deploy (edit locally; NFS propagates), run the engine manually once, wait for 3–5 daily cycles, then:
 ```sql
 -- Portfolio ratio per day (target: drifts from ~2.0 toward 0.8–1.3)
 WITH ranked AS (
  SELECT pfh.pid, pfh.forecast_date, pfh.forecast_units, pfh.lifecycle_phase,
    ROW_NUMBER() OVER (PARTITION BY pfh.pid, pfh.forecast_date ORDER BY fr.started_at DESC) rn
  FROM product_forecasts_history pfh
  JOIN forecast_runs fr ON fr.id = pfh.run_id
  WHERE pfh.forecast_date >= CURRENT_DATE - 7)
 SELECT r.forecast_date, round(SUM(r.forecast_units),0) AS fc,
  SUM(COALESCE(dps.units_sold,0)) AS act,
  round(SUM(r.forecast_units)/NULLIF(SUM(COALESCE(dps.units_sold,0)),0),2) AS ratio
 FROM ranked r
 LEFT JOIN daily_product_snapshots dps ON dps.pid = r.pid AND dps.snapshot_date = r.forecast_date
 WHERE r.rn = 1 AND r.lifecycle_phase != 'dormant'
 GROUP BY 1 ORDER BY 1;
 ```
 Also check `forecast_accuracy` `by_phase` rows for the newest run: decay bias should fall from +0.35 toward ~0, mature from +0.17 toward ~0. (Accuracy lags ~1 day behind each fix since it evaluates yesterday's forecasts.)
 ---
 ## Phase 2 — Demand the model currently ignores or mistimes
 ### F4. Preorder: forecast the preorder rate until arrival, launch curve after
 **Where:** `forecast_engine.py` — `batch_load_product_data()` (add arrival dates), `generate_all_forecasts()` preorder branch (~lines 1005–1009), and `forecast_from_curve()` (or a small wrapper).
 **Problem:** preorder products run the launch curve from `age=0` starting **today**, i.e. full first-week launch sales while the product is still weeks from arriving. Actual preorder-period sales are a much slower trickle.
 **Fix:**
 1. Batch-load each preorder product's expected arrival from `purchase_orders` (line-item grain: it has `pid` and `expected_date` directly). Open statuses verified against live data: `created`, `ordered`, `electronically_sent`, `receiving_started` (~705 open line items currently have a future `expected_date`):
 ```sql
 SELECT pid, MIN(expected_date) AS expected_arrival
 FROM purchase_orders
 WHERE pid = ANY(%s)
  AND status IN ('created', 'ordered', 'electronically_sent', 'receiving_started')
  AND expected_date IS NOT NULL
  AND expected_date >= CURRENT_DATE
 GROUP BY pid
 ```
 Fallbacks, in order: (a) an open PO with a *past* `expected_date` → assume arrival in 7 days; (b) no PO at all → arrival in 14 days (and log a counter of how many hit this default).
 2. In the preorder branch, build the daily array piecewise. Let `days_until_arrival = (expected_arrival - today).days`:
   - Days `0 .. days_until_arrival-1`: flat observed preorder daily rate = `preorder_sales[pid] / max(preorder_days[pid], 1)` (both already batch-loaded), clamped to ≤ the curve's scaled week-0 daily value.
   - Days `days_until_arrival .. horizon`: `forecast_from_curve(curve_info, scale, age_days=0, ...)` shifted so the curve's day 0 lands on the arrival date (i.e. pass `horizon_days - days_until_arrival` and offset into the output array).
   - Keep the existing `compute_scale_factor('preorder', ...)` for the post-arrival curve; the pre-arrival segment doesn't use it.
 This is consistent with how the reference curves were built: historical preorder units were recorded on their **order dates** (pre-arrival), so week-0 of the fitted curves reflects post-receipt orders, not the backlog.
 ### F5. Dormant products: small positive rate instead of hard zero, and count them
 **Where:** `forecast_engine.py` — `generate_all_forecasts()` dormant branch (~lines 1040–1042), `batch_load_product_data()`, and `compute_accuracy()`.
 **Problem:** all ~28K dormant products are forecast at exactly 0, yet they sold 16,180 units in the eval window (~11% of all demand) — restocks, promos, long-tail. Worse, dormant is *excluded* from the headline accuracy filter, so this miss is invisible.
 **Fix (cheap version, do this now):**
 1. Batch-load a trailing-180-day order rate for dormant products (11,362 of them have ≥1 sale in 180d — verified):
 ```sql
 SELECT o.pid, SUM(o.quantity) / 180.0 AS rate
 FROM orders o
 WHERE o.pid = ANY(%s)
  AND o.canceled IS DISTINCT FROM TRUE
  AND o.date >= CURRENT_DATE - INTERVAL '180 days'
 GROUP BY o.pid
 ```
 2. Dormant branch: if the product has a rate > 0, forecast it flat with `method = 'velocity'`; else keep zeros with `method = 'zero'`. Apply the same DOW/seasonal multipliers as everything else (automatic — they're applied after the branch).
 3. In `compute_accuracy()`, add a second overall row: `metric_type='overall', dimension_value='all_incl_dormant'` with no dormant filter (keep the existing `'all'` row unchanged for trend continuity). One extra entry in the `dimensions`/`filter_clauses` dicts.
 **Upgrade path (optional, Phase 4):** replace flat rates for `slow_mover` + dormant-with-sales with TSB (Teunter–Syntetos–Babai), the standard intermittent-demand method with obsolescence handling. Per product over a daily series `d_t` (build it from snapshots the F2 way — full calendar reindex):
 ```
 if d_t > 0:  p_t = p_{t-1} + β·(1 − p_{t-1});  z_t = z_{t-1} + α·(d_t − z_{t-1})
 else:        p_t = p_{t-1}·(1 − β);            z_t = z_{t-1}
 forecast = p_T · z_T   (flat across horizon)
 ```
 Start with α=0.1, β=0.05, initialize p = (nonzero days / total days), z = mean of nonzero demands. Scope: slow_mover (~6K) + dormant with 180d sales (~11K); series from up to 180 days of snapshots (sparse rows → ~manageable volume). Only do this after Phase 3 measurement exists to prove it beats the flat rates.
 ### Phase 2 validation
 After 3–5 cycles: preorder `by_phase` bias should drop from +0.85 toward < +0.3; the new `all_incl_dormant` row should appear and its `total_actual_units` minus `'all'`'s should be largely *covered* rather than all-miss (dormant `bias` rising from −1.36 toward ~−0.3 or better).
 ---
 ## Phase 3 — Fix the measurement (schema + engine + API + UI)
 > Without this phase you cannot see whether Phases 1–2 worked except by ad-hoc SQL, the lead-time chart stays a single bucket forever, and the dashboard keeps displaying a number with a 190% floor in red.
 ### F7. Archive long-lead forecasts so 15/30/60/90d accuracy exists
 **Where:** `forecast_engine.py` — `archive_forecasts()` (~lines 1086–1154), `compute_accuracy()` CTE (~lines 1201–1228).
 **Problem:** the current design archives only *past-dated* rows of the previous run before truncation. With daily runs, that's only ever the 1-day-ahead slice — all 879,800 accuracy samples sit in the '1-7d' bucket and the longer buckets in the UI chart can never populate. Purchasing decisions ride on 30–60d forecasts that are never validated.
 **Fix:**
 1. Keep the existing past-date archiving exactly as is (it provides dense short-lead coverage).
 2. After `generate_all_forecasts()` completes, additionally archive a **sampled set of future leads** from the new run, non-dormant only, attributed to the *current* run id (correct attribution, unlike the past-date path which attributes to the previous run):
 ```sql
 INSERT INTO product_forecasts_history
    (run_id, pid, forecast_date, forecast_units, forecast_revenue,
     lifecycle_phase, forecast_method, confidence_lower, confidence_upper, generated_at)
 SELECT %(run_id)s, pid, forecast_date, forecast_units, forecast_revenue,
    lifecycle_phase, forecast_method, confidence_lower, confidence_upper, generated_at
 FROM product_forecasts
 WHERE lifecycle_phase != 'dormant'
  AND forecast_date - CURRENT_DATE IN (7, 14, 30, 60, 89)
 ON CONFLICT (run_id, pid, forecast_date) DO NOTHING
 ```
 Volume: ~10K non-dormant products × 5 leads ≈ 50K rows/day; the existing 90-day prune (`forecast_date < CURRENT_DATE - 90`) bounds steady state at a few million rows. Note future-dated rows survive until their date passes + 90 days — that's intended.
 3. **CRITICAL companion change** in `compute_accuracy()`: the accuracy CTE must now exclude not-yet-realized rows, or future-dated archives get scored against actual=0:
 ```sql
 FROM product_forecasts_history pfh
 JOIN forecast_runs fr ON fr.id = pfh.run_id
 WHERE pfh.forecast_date < CURRENT_DATE          -- ADD THIS
 ```
 4. **Dedup semantics change.** Today's `ROW_NUMBER() OVER (PARTITION BY pid, forecast_date ORDER BY started_at DESC)` keeps only the latest (= shortest-lead) row per pid/date, which would silently discard all the new long-lead rows. Restructure:
   - Compute `lead_days = forecast_date - started_at::date` and the lead bucket *inside* `ranked_history`.
   - For `by_lead_time`: dedup `PARTITION BY pid, forecast_date, lead_bucket` (one sample per pid/date/bucket, latest run wins within a bucket).
   - For everything else (`overall`, `by_phase`, `by_method`, `daily`, and the new weekly metric below): restrict to `lead_days BETWEEN 0 AND 6` and keep the existing per-(pid, date) dedup. This preserves the current meaning of the headline metrics (short-lead) while the lead-time table becomes real.
 ### F8. Track a naive baseline (forecast value-added)
 **Where:** `archive_forecasts()` (both INSERT paths), `compute_accuracy()`, `forecast_accuracy` schema, `/forecast/accuracy` endpoint.
 **Problem:** the engine currently *loses* to a trailing-average naive forecast (221% vs 204% daily WMAPE) and nothing on the dashboard would ever reveal that. Every accuracy improvement should be judged as value-over-naive.
 **Fix:**
 1. Schema (idempotent, in the ensure blocks): `ALTER TABLE product_forecasts_history ADD COLUMN IF NOT EXISTS naive_units NUMERIC(10,2);` and `ALTER TABLE forecast_accuracy ADD COLUMN IF NOT EXISTS naive_wmape NUMERIC(10,4), ADD COLUMN IF NOT EXISTS fva NUMERIC(10,4);`
 2. Populate `naive_units` during both archive INSERTs via a join — naive = flat trailing-28-day average daily units as of archive time (28 days = DOW-balanced; information available at generation; same value at every lead, which is exactly what a naive baseline means):
 ```sql
 LEFT JOIN (
    SELECT o.pid, SUM(o.quantity) / 28.0 AS naive_daily
    FROM orders o
    WHERE o.canceled IS DISTINCT FROM TRUE
      AND o.date >= CURRENT_DATE - INTERVAL '28 days' AND o.date < CURRENT_DATE
    GROUP BY o.pid
 ) nv ON nv.pid = pf.pid
 -- select COALESCE(nv.naive_daily, 0) AS naive_units
 ```
 3. In `compute_accuracy()`, add to each dimension's aggregate: `SUM(ABS(naive_units - actual_units)) / NULLIF(SUM(actual_units),0) AS naive_wmape` and store `fva = 1 - wmape / naive_wmape` (NULL-safe). Rows archived before this change have `naive_units` NULL — treat NULL as excluded (`FILTER (WHERE naive_units IS NOT NULL)` on the naive sums) rather than as zero.
 4. Endpoint: include `naiveWmape` and `fva` in the `overall` (and per-phase) payload of `/dashboard/forecast/accuracy` in `dashboard.js`.
 ### F9. Weekly-grain headline metric + bias as a percentage
 **Where:** `compute_accuracy()`, `/forecast/accuracy` endpoint, `ForecastAccuracy.tsx`.
 **Problem:** daily-grain WMAPE on this catalog has a ~190% floor — as a headline it's noise. The informative numbers are (a) weekly-per-product WMAPE (currently ~109%, target ~70–85% post-fix) and (b) aggregate bias, which the UI currently renders as `+0.108 units` — indistinguishable from zero while the reality is +70%.
 **Fix:**
 1. New metric in `compute_accuracy()`: `metric_type='overall_weekly', dimension_value='all'`. Definition: using the short-lead deduped rows (lead ≤ 6, non-dormant), aggregate per `(pid, date_trunc('week', forecast_date))` keeping only complete weeks (`COUNT(*) = 7`), then `WMAPE = SUM(ABS(fc_week − act_week)) / SUM(act_week)`, excluding pid-weeks where both are 0. Store sample_size = number of pid-weeks. Compute `naive_wmape`/`fva` the same way from `naive_units`.
 2. Endpoint: expose as `overallWeekly`; also add a weekly variant to the `accuracyTrend` query (`metric_type='overall_weekly'`). The trend will start empty (old runs lack the row) — that's fine; don't backfill.
 3. `ForecastAccuracy.tsx`:
   - Headline WMAPE → `overallWeekly.wmape`, labeled "WMAPE (weekly)". Keep daily WMAPE available in a tooltip if desired.
   - Color thresholds for weekly grain: green ≤ 60, yellow ≤ 90, red above (tunable; document that they're calibrated for intermittent retail demand).
   - Replace the bias row: show `(totalForecast / totalActual − 1)` as a signed percentage labeled "Forecast vs actual" (both totals already arrive in `overall`). Keep MAE.
   - Add a "vs naive" line: naive weekly WMAPE and FVA. FVA > 0 = engine adds value.
   - The lead-time chart needs no code change — buckets will populate as F7 rows mature (7d lead evaluable after 7 days, 30d after 30, etc.).
 4. `confidenceLevel` in `/forecast/metrics` ([dashboard.js ~line 360]) is "share of products forecast via lifecycle curves", not confidence. It only feeds a per-day tooltip field — rename the JSON field to `curveCoverage` and update the one consumer in `ForecastMetrics.tsx`, or leave it and add a comment; low priority.
 ### Phase 3 validation
 - Next run after deploy: `forecast_accuracy` contains `overall_weekly` and `fva` values; `/dashboard/forecast/accuracy` returns them; the overview popover renders weekly WMAPE, bias %, and the naive comparison.
 - After 7/14/30 days: `by_lead_time` rows appear for '8-14d', '15-30d', '31-60d' buckets respectively (61-90d after ~60 days).
 - Confirm engine runtime still < ~5 min and `product_forecasts_history` growth ≈ 50–70K rows/day.
 ---
 ## Phase 4 — Optional / after the above is proven
 - **F6. TSB for slow movers + dormant** (spec in F5). Gate on Phase 3 measurement: ship only if weekly FVA improves on those phases.
 - **F10. Confidence-margin source:** `load_accuracy_margins()` feeds daily-grain per-phase WMAPE (clamped to 1.0) into the intervals, so every interval is ±100% — uninformative. Once `overall_weekly` exists, add per-phase weekly rows (`by_phase_weekly`) and source margins from those instead.
 - **F11.** Update or delete `backfill_accuracy_data()` (it encodes the old formulas). Until then, just don't run `--backfill`.
 - **F12.** `compute_dow_indices()` weights by revenue but the multipliers are applied to units — switch `SUM(o.price * o.quantity)` to `SUM(o.quantity)`. Tiny effect.
 - **F13.** Longer term: for reorder decisions the right target is P(lead-time demand > stock), not a point forecast. Evaluate quantile (pinball) loss at lead-time horizons using the existing confidence-interval columns. Design separately.
 ---
 ## 4. Success criteria
 1. Rolling-14-day portfolio forecast/actual ratio within **0.8–1.25** (currently 1.5–2.5).
 2. Weekly-grain WMAPE ≤ **90%** and **FVA > 0** (engine beats naive) sustained for 2+ weeks.
 3. Decay/preorder/mature per-phase bias within ±0.1 units/day (currently +0.35 / +0.85 / +0.17).
 4. `all_incl_dormant` actuals covered: dormant bias better than −0.4 (currently −1.36, i.e. 100% miss).
 5. Lead-time buckets through 31–60d populated with ≥10K samples each within ~6 weeks.
 6. Launch phase stays healthy (bias within ±0.15, WMAPE not degraded) — regression guard for F3/F4 changes.
 ## 5. Re-measurement appendix
 The naive-vs-engine comparison used in the diagnosis (rerun any time; adjust dates):
 ```sql
 WITH ranked AS (
  SELECT pfh.pid, pfh.forecast_date, pfh.forecast_units, pfh.lifecycle_phase,
    ROW_NUMBER() OVER (PARTITION BY pfh.pid, pfh.forecast_date ORDER BY fr.started_at DESC) rn
  FROM product_forecasts_history pfh
  JOIN forecast_runs fr ON fr.id = pfh.run_id
  WHERE pfh.forecast_date BETWEEN CURRENT_DATE - 9 AND CURRENT_DATE - 1),
 eng AS (SELECT * FROM ranked WHERE rn = 1 AND lifecycle_phase != 'dormant'),
 naive AS (
  SELECT o.pid, SUM(o.quantity)/30.0 AS naive_daily FROM orders o
  WHERE o.canceled IS DISTINCT FROM TRUE
    AND o.date >= CURRENT_DATE - 39 AND o.date < CURRENT_DATE - 9
  GROUP BY o.pid)
 SELECT e.lifecycle_phase, COUNT(*) AS n, SUM(COALESCE(dps.units_sold,0)) AS actual,
  round(SUM(e.forecast_units),0) AS engine_fc, round(SUM(COALESCE(nv.naive_daily,0)),0) AS naive_fc,
  round(SUM(ABS(e.forecast_units - COALESCE(dps.units_sold,0)))/NULLIF(SUM(COALESCE(dps.units_sold,0)),0),2) AS engine_wmape,
  round(SUM(ABS(COALESCE(nv.naive_daily,0) - COALESCE(dps.units_sold,0)))/NULLIF(SUM(COALESCE(dps.units_sold,0)),0),2) AS naive_wmape
 FROM eng e
 LEFT JOIN naive nv ON nv.pid = e.pid
 LEFT JOIN daily_product_snapshots dps ON dps.pid = e.pid AND dps.snapshot_date = e.forecast_date
 GROUP BY ROLLUP(e.lifecycle_phase) ORDER BY 1;
 ```
 Baseline numbers to beat (June 1–9, 2026): engine 221% / naive 204% daily WMAPE; engine_fc/actual = 1.82; per-phase table in §1.
@@ -634,6 +634,52 @@ def forecast_from_curve(curve_params, scale_factor, age_days, horizon_days):
    return np.array(forecasts)
 def forecast_preorder(curve_params, scale_factor, days_until_arrival,
                      preorder_daily_rate, horizon_days):
    """
    Piecewise pre-order forecast: a flat observed pre-order trickle until the
    product is expected to arrive, then the scaled launch curve from age 0.
    The launch curve was fit on POST-receipt order history, so running it from
    today (while the product is still weeks from arriving) front-loads full
    first-week launch volume that hasn't happened yet — the main driver of the
    ~2.15x preorder over-forecast. Instead we forecast the slow pre-order rate
    up to the arrival date, then start the curve's day 0 on that date.
    See FORECAST_FIX_PLAN F4.
    Args:
        curve_params: (amplitude, decay_rate, baseline, ...) weekly curve
        scale_factor: per-product multiplier for the post-arrival curve envelope
        days_until_arrival: calendar days from today until expected arrival
        preorder_daily_rate: observed pre-order units/day (trickle)
        horizon_days: forecast horizon length
    Returns:
        array of daily forecast values of length horizon_days
    """
    amplitude, decay_rate, baseline = curve_params[:3]
    forecasts = np.zeros(horizon_days)
    # Clamp the arrival offset into the horizon
    dua = int(max(0, min(days_until_arrival, horizon_days)))
    # Pre-arrival segment: flat pre-order trickle, capped at the curve's scaled
    # week-0 daily value (a pre-order day shouldn't out-sell the launch peak).
    if dua > 0:
        week0_daily = (amplitude / 7.0) * scale_factor + (baseline / 7.0)
        pre_rate = preorder_daily_rate
        if week0_daily > 0:
            pre_rate = min(pre_rate, week0_daily)
        forecasts[:dua] = max(0.0, pre_rate)
    # Post-arrival segment: scaled launch curve, curve day 0 = arrival date.
    if dua < horizon_days:
        curve_part = forecast_from_curve(curve_params, scale_factor, 0, horizon_days - dua)
        forecasts[dua:] = curve_part
    return forecasts
 # ---------------------------------------------------------------------------
 # Batch data loading (eliminates N+1 per-product queries)
 # ---------------------------------------------------------------------------
@@ -651,9 +697,11 @@ def batch_load_product_data(conn, products):
    data = {
        'preorder_sales': {},
        'preorder_days': {},
        'preorder_arrival_days': {},
        'launch_sales': {},
        'decay_velocity': {},
        'mature_history': {},
        'dormant_rate': {},
    }
    # Pre-order sales: orders placed BEFORE first received date
@@ -677,6 +725,39 @@ def batch_load_product_data(conn, products):
            data['preorder_days'][int(row['pid'])] = float(row['preorder_days'])
        log.info(f"Batch loaded pre-order sales for {len(data['preorder_sales'])}/{len(preorder_pids)} preorder products")
        # Expected arrival per pre-order product, to time the launch curve.
        # Prefer the soonest FUTURE expected_date on an open PO; if the only open
        # PO has a past expected_date assume 7 days; if there's no open PO at all
        # assume 14 days. See FORECAST_FIX_PLAN F4.
        arrival_sql = """
        SELECT pid,
            MIN(expected_date) FILTER (
                WHERE expected_date IS NOT NULL AND expected_date >= CURRENT_DATE
            ) AS future_arrival
        FROM purchase_orders
        WHERE pid = ANY(%s)
          AND status IN ('created', 'ordered', 'electronically_sent', 'receiving_started')
        GROUP BY pid
        """
        adf = execute_query(conn, arrival_sql, [preorder_pids])
        today = date.today()
        for _, row in adf.iterrows():
            pid = int(row['pid'])
            fa = row['future_arrival']
            if pd.notna(fa):
                fa_date = pd.Timestamp(fa).date()
                data['preorder_arrival_days'][pid] = max(0, (fa_date - today).days)
            else:
                data['preorder_arrival_days'][pid] = 7  # open PO, expected_date already past
        no_po = 0
        for pid in preorder_pids:
            if int(pid) not in data['preorder_arrival_days']:
                data['preorder_arrival_days'][int(pid)] = 14  # no open PO at all
                no_po += 1
        log.info(f"Batch loaded preorder arrival for "
                 f"{len(data['preorder_arrival_days']) - no_po}/{len(preorder_pids)} via open POs, "
                 f"{no_po} defaulted to 14d")
    # Launch sales: first 14 days after first received
    launch_pids = products[products['phase'] == 'launch']['pid'].tolist()
    if launch_pids:
@@ -694,15 +775,23 @@ def batch_load_product_data(conn, products):
            data['launch_sales'][int(row['pid'])] = float(row['total_sold'])
        log.info(f"Batch loaded launch sales for {len(data['launch_sales'])}/{len(launch_pids)} launch products")
-    # Decay recent velocity: average daily sales over last 30 days
+    # Decay recent velocity: TRUE calendar-daily average over the last 30 days.
    # We divide the summed units by calendar days (clipped to the product's age),
    # NOT by the number of snapshot rows. Snapshots are sparse and mostly land on
    # sold-days, so AVG(units_sold) averages over sold-days only and inflated the
    # decay rate ~4x (measured 1.353 vs true 0.332 units/day). See FORECAST_FIX_PLAN F1.
    decay_pids = products[products['phase'] == 'decay']['pid'].tolist()
    if decay_pids:
        sql = """
-        SELECT dps.pid, AVG(COALESCE(dps.units_sold, 0)) AS avg_daily
+        SELECT dps.pid,
            SUM(COALESCE(dps.units_sold, 0))::float
              / GREATEST(LEAST(30, (CURRENT_DATE - pm.date_first_received::date)), 1) AS avg_daily
        FROM daily_product_snapshots dps
        JOIN product_metrics pm ON pm.pid = dps.pid
        WHERE dps.pid = ANY(%s)
          AND dps.snapshot_date >= CURRENT_DATE - INTERVAL '30 days'
-        GROUP BY dps.pid
+          AND dps.snapshot_date >= pm.date_first_received::date
        GROUP BY dps.pid, pm.date_first_received
        """
        df = execute_query(conn, sql, [decay_pids])
        for _, row in df.iterrows():
@@ -724,6 +813,25 @@ def batch_load_product_data(conn, products):
            data['mature_history'][int(pid)] = group.copy()
        log.info(f"Batch loaded history for {len(data['mature_history'])}/{len(mature_pids)} mature products")
    # Dormant trailing order rate: dormant products forecast 0 by default, but
    # ~11K of them still sell (restocks, promos, long-tail) — ~11% of all demand
    # currently forecast as a hard zero. Load a trailing-180-day daily order rate
    # so the dormant branch can carry a small positive rate. See FORECAST_FIX_PLAN F5.
    dormant_pids = products[products['phase'] == 'dormant']['pid'].tolist()
    if dormant_pids:
        sql = """
        SELECT o.pid, SUM(o.quantity) / 180.0 AS rate
        FROM orders o
        WHERE o.pid = ANY(%s)
          AND o.canceled IS DISTINCT FROM TRUE
          AND o.date >= CURRENT_DATE - INTERVAL '180 days'
        GROUP BY o.pid
        """
        df = execute_query(conn, sql, [dormant_pids])
        for _, row in df.iterrows():
            data['dormant_rate'][int(row['pid'])] = float(row['rate'])
        log.info(f"Batch loaded dormant order rate for {len(data['dormant_rate'])}/{len(dormant_pids)} dormant products")
    return data
@@ -829,11 +937,20 @@ def forecast_mature(product, history_df):
        # Not enough data — flat velocity
        return np.full(FORECAST_HORIZON_DAYS, velocity)
-    # Fill date gaps with 0 sales (days where product had no snapshot = no sales)
+    # Reindex over the FULL calendar window ending yesterday, not just the span
    # between the first and last snapshot. resample() only covers first→last
    # snapshot, so leading/trailing quiet periods are absent and the Holt level
    # is fitted only on the product's busy span (can run ~4x too high). An
    # explicit reindex fills every quiet calendar day with 0. (pid, snapshot_date)
    # is unique so there is no duplicate-index risk; do NOT use combine_first
    # (it keeps zeros over real data). See FORECAST_FIX_PLAN F2.
    hist = history_df.copy()
    hist['snapshot_date'] = pd.to_datetime(hist['snapshot_date'])
-    hist = hist.set_index('snapshot_date').resample('D').sum().fillna(0)
+    hist = hist.set_index('snapshot_date')['units_sold']
-    series = hist['units_sold'].values.astype(float)
+    full_index = pd.date_range(
        end=pd.Timestamp(date.today() - timedelta(days=1)),
        periods=EXP_SMOOTHING_WINDOW, freq='D')
    series = hist.reindex(full_index, fill_value=0.0).values.astype(float)
    # Need at least 2 non-zero values for smoothing
    if np.count_nonzero(series) < 2:
@@ -956,9 +1073,24 @@ def generate_all_forecasts(conn, curves_df, dow_indices, monthly_indices=None,
    today = date.today()
    forecast_dates = [today + timedelta(days=i) for i in range(FORECAST_HORIZON_DAYS)]
-    # Pre-compute DOW and seasonal multipliers for each forecast date
+    # Pre-compute DOW and seasonal multipliers for each forecast date.
    # DOW multipliers stay ABSOLUTE — every calibration is a multi-week average
    # and therefore DOW-neutral, so reshaping by absolute DOW indices is correct.
    # Seasonal indices must be applied RELATIVE to the calibration period:
    # each per-product calibration (decay velocity, mature Holt level, launch /
    # preorder scale) is fitted on raw recent actuals that already embed the
    # current month's seasonal level. Multiplying by the absolute target-month
    # index double-counts seasonality (~25% over-forecast at the May→June sale
    # transition, worse near November). Divide by the trailing-30-day average
    # index so only the seasonal *change* from calibration to target applies.
    # See FORECAST_FIX_PLAN F3.
    dow_multipliers = [dow_indices.get(d.isoweekday(), 1.0) for d in forecast_dates]
-    seasonal_multipliers = [monthly_indices.get(d.month, 1.0) for d in forecast_dates]
+    trailing = [today - timedelta(days=i) for i in range(1, 31)]
    calibration_index = float(np.mean([monthly_indices.get(d.month, 1.0) for d in trailing]))
    seasonal_multipliers = [
        monthly_indices.get(d.month, 1.0) / max(calibration_index, 0.1)
        for d in forecast_dates
    ]
    # TRUNCATE before streaming writes
    with conn.cursor() as cur:
@@ -1002,9 +1134,33 @@ def generate_all_forecasts(conn, curves_df, dow_indices, monthly_indices=None,
        try:
            curve_info = get_curve_for_product(product, curves_df)
-            if phase in ('preorder', 'launch'):
+            if phase == 'preorder':
                if curve_info:
-                    scale = compute_scale_factor(phase, product, curve_info, batch_data)
+                    scale = compute_scale_factor('preorder', product, curve_info, batch_data)
                    # Time the launch curve to expected arrival instead of
                    # running it from today (F4). Pre-arrival days carry the
                    # observed pre-order trickle rate.
                    days_until_arrival = batch_data['preorder_arrival_days'].get(pid, 14)
                    preorder_units = batch_data['preorder_sales'].get(pid, 0)
                    preorder_days = batch_data['preorder_days'].get(pid, 1)
                    preorder_daily_rate = preorder_units / max(preorder_days, 1)
                    forecasts = forecast_preorder(
                        curve_info, scale, days_until_arrival,
                        preorder_daily_rate, FORECAST_HORIZON_DAYS)
                    method = 'lifecycle_curve'
                else:
                    # No reliable curve — fall back to velocity if available
                    velocity = product.get('sales_velocity_daily') or 0
                    if velocity > 0:
                        forecasts = np.full(FORECAST_HORIZON_DAYS, velocity)
                        method = 'velocity'
                    else:
                        forecasts = forecast_dormant()
                        method = 'zero'
            elif phase == 'launch':
                if curve_info:
                    scale = compute_scale_factor('launch', product, curve_info, batch_data)
                    forecasts = forecast_from_curve(curve_info, scale, age, FORECAST_HORIZON_DAYS)
                    method = 'lifecycle_curve'
                else:
@@ -1038,6 +1194,14 @@ def generate_all_forecasts(conn, curves_df, dow_indices, monthly_indices=None,
                method = 'velocity'
            else:  # dormant
                # Carry a small positive rate for dormant products that still
                # trickle sales (restocks/promos/long-tail); only truly dead
                # products stay at zero. See FORECAST_FIX_PLAN F5.
                rate = batch_data['dormant_rate'].get(pid, 0)
                if rate > 0:
                    forecasts = np.full(FORECAST_HORIZON_DAYS, rate)
                    method = 'velocity'
                else:
                    forecasts = forecast_dormant()
                    method = 'zero'
@@ -1108,6 +1272,8 @@ def archive_forecasts(conn, run_id):
        """)
        cur.execute("CREATE INDEX IF NOT EXISTS idx_pfh_date ON product_forecasts_history(forecast_date)")
        cur.execute("CREATE INDEX IF NOT EXISTS idx_pfh_pid_date ON product_forecasts_history(pid, forecast_date)")
        # Naive-baseline column for forecast value-added (FVA). See FORECAST_FIX_PLAN F8.
        cur.execute("ALTER TABLE product_forecasts_history ADD COLUMN IF NOT EXISTS naive_units NUMERIC(10,2)")
        # Find the previous completed run (whose forecasts are still in product_forecasts)
        cur.execute("""
@@ -1124,15 +1290,27 @@ def archive_forecasts(conn, run_id):
        prev_run_id = prev_run[0]
-        # Archive only past-date forecasts (where actuals now exist)
+        # Archive only past-date forecasts (where actuals now exist). Attach the
        # naive baseline (flat trailing-28-day daily average) at the same time so
        # forecast value-added can be measured. See FORECAST_FIX_PLAN F8.
        cur.execute("""
            INSERT INTO product_forecasts_history
                (run_id, pid, forecast_date, forecast_units, forecast_revenue,
-                 lifecycle_phase, forecast_method, confidence_lower, confidence_upper, generated_at)
+                 lifecycle_phase, forecast_method, confidence_lower, confidence_upper,
-            SELECT %s, pid, forecast_date, forecast_units, forecast_revenue,
+                 generated_at, naive_units)
-                lifecycle_phase, forecast_method, confidence_lower, confidence_upper, generated_at
+            SELECT %s, pf.pid, pf.forecast_date, pf.forecast_units, pf.forecast_revenue,
-            FROM product_forecasts
+                pf.lifecycle_phase, pf.forecast_method, pf.confidence_lower, pf.confidence_upper,
-            WHERE forecast_date < CURRENT_DATE
+                pf.generated_at, COALESCE(nv.naive_daily, 0)
            FROM product_forecasts pf
            LEFT JOIN (
                SELECT o.pid, SUM(o.quantity) / 28.0 AS naive_daily
                FROM orders o
                WHERE o.canceled IS DISTINCT FROM TRUE
                  AND o.date >= CURRENT_DATE - INTERVAL '28 days'
                  AND o.date < CURRENT_DATE
                GROUP BY o.pid
            ) nv ON nv.pid = pf.pid
            WHERE pf.forecast_date < CURRENT_DATE
            ON CONFLICT (run_id, pid, forecast_date) DO NOTHING
        """, (prev_run_id,))
@@ -1154,6 +1332,48 @@ def archive_forecasts(conn, run_id):
        return archived
 def archive_future_leads(conn, run_id):
    """
    Archive a sampled set of FUTURE-lead forecasts from the just-generated
    product_forecasts, attributed to the current run.
    The past-date archive in archive_forecasts() only ever captures the 1-day
    slice that just elapsed, so every accuracy sample lands in the '1-7d' lead
    bucket and the 15/30/60/90-day forecasts that purchasing actually rides on
    are never validated. Here we snapshot the 7/14/30/60/89-day-ahead leads
    (non-dormant) so that, once each date passes, compute_accuracy() can score
    them in their lead bucket. The naive baseline is attached the same way as in
    the past-date path. Future-dated rows survive the 90-day prune until their
    own date passes. See FORECAST_FIX_PLAN F7.
    """
    with conn.cursor() as cur:
        cur.execute("""
            INSERT INTO product_forecasts_history
                (run_id, pid, forecast_date, forecast_units, forecast_revenue,
                 lifecycle_phase, forecast_method, confidence_lower, confidence_upper,
                 generated_at, naive_units)
            SELECT %s, pf.pid, pf.forecast_date, pf.forecast_units, pf.forecast_revenue,
                pf.lifecycle_phase, pf.forecast_method, pf.confidence_lower, pf.confidence_upper,
                pf.generated_at, COALESCE(nv.naive_daily, 0)
            FROM product_forecasts pf
            LEFT JOIN (
                SELECT o.pid, SUM(o.quantity) / 28.0 AS naive_daily
                FROM orders o
                WHERE o.canceled IS DISTINCT FROM TRUE
                  AND o.date >= CURRENT_DATE - INTERVAL '28 days'
                  AND o.date < CURRENT_DATE
                GROUP BY o.pid
            ) nv ON nv.pid = pf.pid
            WHERE pf.lifecycle_phase != 'dormant'
              AND pf.forecast_date - CURRENT_DATE IN (7, 14, 30, 60, 89)
            ON CONFLICT (run_id, pid, forecast_date) DO NOTHING
        """, (run_id,))
        archived = cur.rowcount
    conn.commit()
    log.info(f"Archived {archived} future-lead forecast rows (7/14/30/60/89d) for run {run_id}")
    return archived
 def compute_accuracy(conn, run_id):
    """
    Compute forecast accuracy metrics from archived history vs. actual sales.
@@ -1162,11 +1382,18 @@ def compute_accuracy(conn, run_id):
    (pid, forecast_date = snapshot_date) to compare forecasted vs. actual units.
    Stores results in forecast_accuracy table, broken down by:
-      - overall: single aggregate row
+      - overall: two rows — 'all' (non-dormant) and 'all_incl_dormant' (F5)
      - overall_weekly: per-product weekly-grain WMAPE — the informative headline
        for intermittent demand (daily grain has a ~190% floor) (F9)
      - by_phase: per lifecycle phase
-      - by_lead_time: bucketed by how far ahead the forecast was
+      - by_lead_time: bucketed by how far ahead the forecast was — long-lead
        buckets populate as the future-lead archives mature (F7)
      - by_method: per forecast method
      - daily: per forecast_date (for trend charts)
    Every dimension also stores naive_wmape (flat trailing-28d baseline) and
    fva = 1 - wmape/naive_wmape, so the engine can be judged as value-over-naive
    (F8). Only realized dates (forecast_date < CURRENT_DATE) are scored.
    """
    with conn.cursor() as cur:
        # Ensure accuracy table exists
@@ -1186,6 +1413,10 @@ def compute_accuracy(conn, run_id):
                PRIMARY KEY (run_id, metric_type, dimension_value)
            )
        """)
        # Naive-baseline WMAPE and forecast value-added (FVA = 1 - wmape/naive_wmape).
        # See FORECAST_FIX_PLAN F8.
        cur.execute("ALTER TABLE forecast_accuracy ADD COLUMN IF NOT EXISTS naive_wmape NUMERIC(10,4)")
        cur.execute("ALTER TABLE forecast_accuracy ADD COLUMN IF NOT EXISTS fva NUMERIC(10,4)")
        conn.commit()
        # Check if we have any history to analyze
@@ -1195,109 +1426,109 @@ def compute_accuracy(conn, run_id):
            log.info("No forecast history available for accuracy computation")
            return
-        # For each (pid, forecast_date) pair, keep only the most recent run's
+        # Base CTEs (FORECAST_FIX_PLAN F7):
-        # forecast row. This prevents double-counting when multiple runs have
+        #  - Only score realized dates (forecast_date < CURRENT_DATE); future-lead
-        # archived forecasts for the same product×date combination.
+        #    archives are excluded until their date passes.
-        accuracy_cte = """
+        #  - short_lead*: lead 0-6 deduped per (pid, forecast_date) — preserves the
-        WITH ranked_history AS (
+        #    meaning of the existing headline metrics. short_lead_eval keeps the
        #    raw snapshot grid (incl. zero-zero days) for complete-week detection;
        #    `accuracy` drops zero-zero days for daily-grain metrics.
        #  - lead_dedup/lead_accuracy: deduped per (pid, forecast_date, lead_bucket)
        #    so each long-lead bucket gets its own sample (the by_lead_time table).
        base_cte = """
        WITH ranked_all AS (
            SELECT
-                pfh.*,
+                pfh.pid, pfh.forecast_date, pfh.forecast_units, pfh.naive_units,
                pfh.lifecycle_phase, pfh.forecast_method,
                fr.started_at,
-                ROW_NUMBER() OVER (
+                (pfh.forecast_date - fr.started_at::date) AS lead_days,
-                    PARTITION BY pfh.pid, pfh.forecast_date
+                CASE
-                    ORDER BY fr.started_at DESC
+                    WHEN (pfh.forecast_date - fr.started_at::date) BETWEEN 0 AND 6 THEN '1-7d'
-                ) AS rn
+                    WHEN (pfh.forecast_date - fr.started_at::date) BETWEEN 7 AND 13 THEN '8-14d'
                    WHEN (pfh.forecast_date - fr.started_at::date) BETWEEN 14 AND 29 THEN '15-30d'
                    WHEN (pfh.forecast_date - fr.started_at::date) BETWEEN 30 AND 59 THEN '31-60d'
                    ELSE '61-90d'
                END AS lead_bucket
            FROM product_forecasts_history pfh
            JOIN forecast_runs fr ON fr.id = pfh.run_id
            WHERE pfh.forecast_date < CURRENT_DATE
        ),
        short_lead AS (
            SELECT *,
                ROW_NUMBER() OVER (
                    PARTITION BY pid, forecast_date ORDER BY started_at DESC
                ) AS rn
            FROM ranked_all
            WHERE lead_days BETWEEN 0 AND 6
        ),
        short_lead_eval AS (
            SELECT sl.pid, sl.lifecycle_phase, sl.forecast_method, sl.forecast_date,
                sl.forecast_units, sl.naive_units,
                COALESCE(dps.units_sold, 0) AS actual_units,
                (sl.forecast_units - COALESCE(dps.units_sold, 0)) AS error,
                ABS(sl.forecast_units - COALESCE(dps.units_sold, 0)) AS abs_error
            FROM short_lead sl
            LEFT JOIN daily_product_snapshots dps
                ON dps.pid = sl.pid AND dps.snapshot_date = sl.forecast_date
            WHERE sl.rn = 1
        ),
        accuracy AS (
-            SELECT
+            SELECT * FROM short_lead_eval
-                rh.lifecycle_phase,
+            WHERE NOT (forecast_units = 0 AND actual_units = 0)
-                rh.forecast_method,
+        ),
-                rh.forecast_date,
+        lead_dedup AS (
-                (rh.forecast_date - rh.started_at::date) AS lead_days,
+            SELECT *,
-                rh.forecast_units,
+                ROW_NUMBER() OVER (
                    PARTITION BY pid, forecast_date, lead_bucket ORDER BY started_at DESC
                ) AS rn
            FROM ranked_all
        ),
        lead_accuracy AS (
            SELECT ld.lead_bucket, ld.forecast_units, ld.naive_units,
                COALESCE(dps.units_sold, 0) AS actual_units,
-                (rh.forecast_units - COALESCE(dps.units_sold, 0)) AS error,
+                (ld.forecast_units - COALESCE(dps.units_sold, 0)) AS error,
-                ABS(rh.forecast_units - COALESCE(dps.units_sold, 0)) AS abs_error
+                ABS(ld.forecast_units - COALESCE(dps.units_sold, 0)) AS abs_error
-            FROM ranked_history rh
+            FROM lead_dedup ld
            LEFT JOIN daily_product_snapshots dps
-                ON dps.pid = rh.pid AND dps.snapshot_date = rh.forecast_date
+                ON dps.pid = ld.pid AND dps.snapshot_date = ld.forecast_date
-            WHERE rh.rn = 1
+            WHERE ld.rn = 1
-              AND NOT (rh.forecast_units = 0 AND COALESCE(dps.units_sold, 0) = 0)
+              AND ld.lifecycle_phase != 'dormant'
              AND NOT (ld.forecast_units = 0 AND COALESCE(dps.units_sold, 0) = 0)
        )
        """
-        # Compute and insert metrics for each dimension
+        # Daily-grain aggregate over a source CTE aliased `a`, computing the
-        dimensions = {
+        # engine WMAPE plus the naive-baseline WMAPE (NULL-safe: rows archived
-            'overall': "SELECT 'all' AS dim",
+        # before F8 have naive_units NULL and are excluded from the naive sums).
-            'by_phase': "SELECT DISTINCT lifecycle_phase AS dim FROM accuracy",
+        def daily_agg(dim_expr, source, where=None, group_by=None):
-            'by_lead_time': """
+            where_sql = f"WHERE {where}" if where else ""
-                SELECT DISTINCT
+            group_sql = f"GROUP BY {group_by}" if group_by else ""
-                    CASE
+            return f"""
                        WHEN lead_days BETWEEN 0 AND 6 THEN '1-7d'
                        WHEN lead_days BETWEEN 7 AND 13 THEN '8-14d'
                        WHEN lead_days BETWEEN 14 AND 29 THEN '15-30d'
                        WHEN lead_days BETWEEN 30 AND 59 THEN '31-60d'
                        ELSE '61-90d'
                    END AS dim
                FROM accuracy
            """,
            'by_method': "SELECT DISTINCT forecast_method AS dim FROM accuracy",
            'daily': "SELECT DISTINCT forecast_date::text AS dim FROM accuracy",
        }
        filter_clauses = {
            'overall': "lifecycle_phase != 'dormant'",
            'by_phase': "lifecycle_phase = dims.dim",
            'by_lead_time': """
                CASE
                    WHEN lead_days BETWEEN 0 AND 6 THEN '1-7d'
                    WHEN lead_days BETWEEN 7 AND 13 THEN '8-14d'
                    WHEN lead_days BETWEEN 14 AND 29 THEN '15-30d'
                    WHEN lead_days BETWEEN 30 AND 59 THEN '31-60d'
                    ELSE '61-90d'
                END = dims.dim
            """,
            'by_method': "forecast_method = dims.dim",
            'daily': "forecast_date::text = dims.dim",
        }
        total_inserted = 0
        for metric_type, dim_query in dimensions.items():
            filter_clause = filter_clauses[metric_type]
            sql = f"""
            {accuracy_cte},
            dims AS ({dim_query})
            SELECT
-                dims.dim,
+                {dim_expr} AS dim,
                COUNT(*) AS sample_size,
                COALESCE(SUM(a.actual_units), 0) AS total_actual,
                COALESCE(SUM(a.forecast_units), 0) AS total_forecast,
                AVG(a.abs_error) AS mae,
                CASE WHEN SUM(a.actual_units) > 0
-                    THEN SUM(a.abs_error) / SUM(a.actual_units)
+                    THEN SUM(a.abs_error) / SUM(a.actual_units) ELSE NULL END AS wmape,
                    ELSE NULL END AS wmape,
                AVG(a.error) AS bias,
-                SQRT(AVG(POWER(a.error, 2))) AS rmse
+                SQRT(AVG(POWER(a.error, 2))) AS rmse,
-            FROM dims
+                CASE WHEN SUM(a.actual_units) FILTER (WHERE a.naive_units IS NOT NULL) > 0
-            CROSS JOIN accuracy a
+                    THEN SUM(ABS(a.naive_units - a.actual_units)) FILTER (WHERE a.naive_units IS NOT NULL)
-            WHERE {filter_clause}
+                         / SUM(a.actual_units) FILTER (WHERE a.naive_units IS NOT NULL)
-            GROUP BY dims.dim
+                    ELSE NULL END AS naive_wmape
            FROM {source} a
            {where_sql}
            {group_sql}
            """
-            cur.execute(sql)
+        insert_sql = """
            rows = cur.fetchall()
            for row in rows:
                dim_val, sample_size, total_actual, total_forecast, mae, wmape, bias, rmse = row
                cur.execute("""
            INSERT INTO forecast_accuracy
                (run_id, metric_type, dimension_value, sample_size,
-                         total_actual_units, total_forecast_units, mae, wmape, bias, rmse)
+                 total_actual_units, total_forecast_units, mae, wmape, bias, rmse,
-                    VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
+                 naive_wmape, fva)
            VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
            ON CONFLICT (run_id, metric_type, dimension_value)
            DO UPDATE SET
                sample_size = EXCLUDED.sample_size,
@@ -1305,14 +1536,89 @@ def compute_accuracy(conn, run_id):
                total_forecast_units = EXCLUDED.total_forecast_units,
                mae = EXCLUDED.mae, wmape = EXCLUDED.wmape,
                bias = EXCLUDED.bias, rmse = EXCLUDED.rmse,
                naive_wmape = EXCLUDED.naive_wmape, fva = EXCLUDED.fva,
                computed_at = NOW()
-                """, (run_id, metric_type, dim_val, sample_size,
+        """
-                      float(total_actual), float(total_forecast),
+
-                      float(mae) if mae is not None else None,
+        def _f(x):
-                      float(wmape) if wmape is not None else None,
+            return float(x) if x is not None else None
-                      float(bias) if bias is not None else None,
+
-                      float(rmse) if rmse is not None else None))
+        def run_and_insert(metric_type, sql):
-                total_inserted += 1
+            cur.execute(base_cte + sql)
            n = 0
            for row in cur.fetchall():
                (dim_val, sample_size, total_actual, total_forecast,
                 mae, wmape, bias, rmse, naive_wmape) = row
                fva = None
                if wmape is not None and naive_wmape is not None and float(naive_wmape) > 0:
                    fva = 1.0 - float(wmape) / float(naive_wmape)
                cur.execute(insert_sql, (
                    run_id, metric_type, dim_val, sample_size,
                    _f(total_actual), _f(total_forecast), _f(mae), _f(wmape),
                    _f(bias), _f(rmse), _f(naive_wmape), _f(fva)))
                n += 1
            return n
        total_inserted = 0
        # overall: two rows — 'all' (non-dormant, the headline) and
        # 'all_incl_dormant' (everything, so the ~11% dormant demand stops being
        # invisible). Both are short-lead (lead 0-6). F5.
        overall_source = """(
                SELECT a.*, 'all'::text AS dim FROM accuracy a WHERE a.lifecycle_phase != 'dormant'
                UNION ALL
                SELECT a.*, 'all_incl_dormant'::text AS dim FROM accuracy a
            )"""
        total_inserted += run_and_insert('overall',
            daily_agg('a.dim', overall_source, group_by='a.dim'))
        # by_phase / by_method / daily — short-lead daily-grain over `accuracy`.
        total_inserted += run_and_insert('by_phase',
            daily_agg('a.lifecycle_phase', 'accuracy', group_by='a.lifecycle_phase'))
        total_inserted += run_and_insert('by_method',
            daily_agg('a.forecast_method', 'accuracy', group_by='a.forecast_method'))
        total_inserted += run_and_insert('daily',
            daily_agg('a.forecast_date::text', 'accuracy',
                      where="a.lifecycle_phase != 'dormant'", group_by='a.forecast_date'))
        # by_lead_time — one sample per (pid, date, lead bucket) over `lead_accuracy`.
        # Buckets beyond '1-7d' populate as the future-lead archives (F7) mature.
        total_inserted += run_and_insert('by_lead_time',
            daily_agg('a.lead_bucket', 'lead_accuracy', group_by='a.lead_bucket'))
        # overall_weekly — the informative headline for intermittent retail demand.
        # Aggregate the short-lead rows to (pid, complete week), then WMAPE over
        # pid-weeks. Daily-grain WMAPE has a ~190% floor on this catalog; weekly
        # grain is ~109% and responds to real improvement. F9.
        weekly_sql = """,
        weekly AS (
            SELECT pid, date_trunc('week', forecast_date) AS wk,
                SUM(forecast_units) AS fc_week,
                SUM(actual_units) AS act_week,
                SUM(naive_units) AS naive_week,
                bool_and(naive_units IS NOT NULL) AS naive_complete
            FROM short_lead_eval
            WHERE lifecycle_phase != 'dormant'
            GROUP BY pid, date_trunc('week', forecast_date)
            HAVING COUNT(*) = 7
        )
        SELECT 'all'::text AS dim,
            COUNT(*) AS sample_size,
            COALESCE(SUM(act_week), 0) AS total_actual,
            COALESCE(SUM(fc_week), 0) AS total_forecast,
            AVG(ABS(fc_week - act_week)) AS mae,
            CASE WHEN SUM(act_week) > 0
                THEN SUM(ABS(fc_week - act_week)) / SUM(act_week) ELSE NULL END AS wmape,
            AVG(fc_week - act_week) AS bias,
            SQRT(AVG(POWER(fc_week - act_week, 2))) AS rmse,
            CASE WHEN SUM(act_week) FILTER (WHERE naive_complete) > 0
                THEN SUM(ABS(naive_week - act_week)) FILTER (WHERE naive_complete)
                     / SUM(act_week) FILTER (WHERE naive_complete)
                ELSE NULL END AS naive_wmape
        FROM weekly
        WHERE NOT (fc_week = 0 AND act_week = 0)
        """
        total_inserted += run_and_insert('overall_weekly', weekly_sql)
        conn.commit()
@@ -1562,6 +1868,10 @@ def main():
            conn, curves_df, dow_indices, monthly_indices, accuracy_margins
        )
        # Phase 4b: Snapshot sampled future-lead forecasts (7/14/30/60/89d) from
        # the fresh run so long-lead accuracy populates once those dates pass (F7).
        archive_future_leads(conn, run_id)
        duration = time.time() - start_time
        # Record run completion (include DOW indices in metadata)
@@ -357,6 +357,9 @@ router.get('/forecast/metrics', async (req, res) => {
            const active = parseInt(totals.active_products) || 1;
            const curveProducts = parseInt(totals.curve_products) || 0;
            // NOTE: despite the name, this is "share of active products forecast via
            // lifecycle curves" (curve coverage), NOT a statistical confidence. It only
            // feeds a per-day tooltip field. See FORECAST_FIX_PLAN F9 (point 4).
            const confidenceLevel = parseFloat((curveProducts / active).toFixed(2));
            // Daily series from actual forecast
@@ -687,14 +690,29 @@ router.get('/forecast/accuracy', async (req, res) => {
        const { rows: metrics } = await executeQuery(`
            SELECT metric_type, dimension_value, sample_size,
                total_actual_units, total_forecast_units,
-                mae, wmape, bias, rmse
+                mae, wmape, bias, rmse, naive_wmape, fva
            FROM forecast_accuracy
            WHERE run_id = $1
            ORDER BY metric_type, dimension_value
        `, [latestRunId]);
        // Shared shaping for an "overall"-style aggregate row (daily or weekly grain).
        const shapeOverall = (m) => m ? {
            sampleSize: parseInt(m.sample_size),
            totalActual: parseFloat(m.total_actual_units) || 0,
            totalForecast: parseFloat(m.total_forecast_units) || 0,
            mae: m.mae != null ? parseFloat(parseFloat(m.mae).toFixed(4)) : null,
            wmape: m.wmape != null ? parseFloat((parseFloat(m.wmape) * 100).toFixed(1)) : null,
            bias: m.bias != null ? parseFloat(parseFloat(m.bias).toFixed(4)) : null,
            rmse: m.rmse != null ? parseFloat(parseFloat(m.rmse).toFixed(4)) : null,
            naiveWmape: m.naive_wmape != null ? parseFloat((parseFloat(m.naive_wmape) * 100).toFixed(1)) : null,
            fva: m.fva != null ? parseFloat(parseFloat(m.fva).toFixed(3)) : null,
        } : null;
        // Organize into response structure
-        const overall = metrics.find(m => m.metric_type === 'overall');
+        const overall = metrics.find(m => m.metric_type === 'overall' && m.dimension_value === 'all')
        const overallInclDormant = metrics.find(m => m.metric_type === 'overall' && m.dimension_value === 'all_incl_dormant')
        const overallWeekly = metrics.find(m => m.metric_type === 'overall_weekly');
        const byPhase = metrics
            .filter(m => m.metric_type === 'by_phase')
            .map(m => ({
@@ -706,6 +724,8 @@ router.get('/forecast/accuracy', async (req, res) => {
                wmape: m.wmape != null ? parseFloat((parseFloat(m.wmape) * 100).toFixed(1)) : null,
                bias: m.bias != null ? parseFloat(parseFloat(m.bias).toFixed(4)) : null,
                rmse: m.rmse != null ? parseFloat(parseFloat(m.rmse).toFixed(4)) : null,
                naiveWmape: m.naive_wmape != null ? parseFloat((parseFloat(m.naive_wmape) * 100).toFixed(1)) : null,
                fva: m.fva != null ? parseFloat(parseFloat(m.fva).toFixed(3)) : null,
            }))
            .sort((a, b) => (b.totalActual || 0) - (a.totalActual || 0));
@@ -763,6 +783,26 @@ router.get('/forecast/accuracy', async (req, res) => {
            sampleSize: parseInt(r.sample_size),
        }));
        // Weekly-grain trend across runs (starts empty for old runs that predate
        // the overall_weekly metric — that's expected, no backfill). F9.
        const { rows: weeklyTrendRows } = await executeQuery(`
            SELECT fr.finished_at::date AS run_date,
                fa.wmape, fa.naive_wmape, fa.fva, fa.sample_size
            FROM forecast_accuracy fa
            JOIN forecast_runs fr ON fr.id = fa.run_id
            WHERE fa.metric_type = 'overall_weekly'
              AND fa.dimension_value = 'all'
            ORDER BY fr.finished_at
        `);
        const accuracyTrendWeekly = weeklyTrendRows.map(r => ({
            date: r.run_date instanceof Date ? r.run_date.toISOString().split('T')[0] : r.run_date,
            wmape: r.wmape != null ? parseFloat((parseFloat(r.wmape) * 100).toFixed(1)) : null,
            naiveWmape: r.naive_wmape != null ? parseFloat((parseFloat(r.naive_wmape) * 100).toFixed(1)) : null,
            fva: r.fva != null ? parseFloat(parseFloat(r.fva).toFixed(3)) : null,
            sampleSize: parseInt(r.sample_size),
        }));
        res.json({
            hasData: true,
            computedAt,
@@ -775,20 +815,15 @@ router.get('/forecast/accuracy', async (req, res) => {
                    ? historyInfo.latest_date.toISOString().split('T')[0]
                    : historyInfo.latest_date,
            },
-            overall: overall ? {
+            overall: shapeOverall(overall),
-                sampleSize: parseInt(overall.sample_size),
+            overallInclDormant: shapeOverall(overallInclDormant),
-                totalActual: parseFloat(overall.total_actual_units) || 0,
+            overallWeekly: shapeOverall(overallWeekly),
                totalForecast: parseFloat(overall.total_forecast_units) || 0,
                mae: overall.mae != null ? parseFloat(parseFloat(overall.mae).toFixed(4)) : null,
                wmape: overall.wmape != null ? parseFloat((parseFloat(overall.wmape) * 100).toFixed(1)) : null,
                bias: overall.bias != null ? parseFloat(parseFloat(overall.bias).toFixed(4)) : null,
                rmse: overall.rmse != null ? parseFloat(parseFloat(overall.rmse).toFixed(4)) : null,
            } : null,
            byPhase,
            byLeadTime,
            byMethod,
            dailyTrend,
            accuracyTrend,
            accuracyTrendWeekly,
        });
    } catch (err) {
        console.error('Error fetching forecast accuracy:', err);
@@ -2,7 +2,7 @@ import { useQuery } from "@tanstack/react-query"
 import { apiFetch } from '@/utils/api';
 import { BarChart, Bar, ResponsiveContainer, XAxis, YAxis, Tooltip as RechartsTooltip, Cell, LineChart, Line } from "recharts"
 import config from "@/config"
-import { Target, TrendingDown, ArrowUpDown } from "lucide-react"
+import { Target, TrendingDown, ArrowUpDown, Swords } from "lucide-react"
 import { Tooltip as UITooltip, TooltipContent, TooltipProvider, TooltipTrigger } from "@/components/ui/tooltip"
 import { PHASE_CONFIG } from "@/utils/lifecyclePhases"
@@ -14,6 +14,8 @@ interface OverallMetrics {
  wmape: number | null
  bias: number | null
  rmse: number | null
  naiveWmape?: number | null
  fva?: number | null
 }
 interface PhaseAccuracy {
@@ -25,6 +27,8 @@ interface PhaseAccuracy {
  wmape: number | null
  bias: number | null
  rmse: number | null
  naiveWmape?: number | null
  fva?: number | null
 }
 interface LeadTimeAccuracy {
@@ -51,11 +55,14 @@ interface AccuracyData {
  daysOfHistory?: number
  historyRange?: { from: string; to: string }
  overall?: OverallMetrics
  overallInclDormant?: OverallMetrics
  overallWeekly?: OverallMetrics
  byPhase?: PhaseAccuracy[]
  byLeadTime?: LeadTimeAccuracy[]
  byMethod?: { method: string; sampleSize: number; mae: number | null; wmape: number | null; bias: number | null }[]
  dailyTrend?: { date: string; mae: number | null; wmape: number | null; bias: number | null }[]
  accuracyTrend?: AccuracyTrendPoint[]
  accuracyTrendWeekly?: { date: string; wmape: number | null; naiveWmape: number | null; fva: number | null; sampleSize: number }[]
 }
 function MetricSkeleton() {
@@ -74,12 +81,30 @@ function formatBias(bias: number | null): string {
 }
 function getAccuracyColor(wmape: number | null): string {
  // Daily-grain thresholds (used for the by-phase / lead-time bars).
  if (wmape === null) return "text-muted-foreground"
  if (wmape <= 30) return "text-green-600"
  if (wmape <= 50) return "text-yellow-600"
  return "text-red-600"
 }
 function getWeeklyAccuracyColor(wmape: number | null): string {
  // Weekly per-product grain has a much lower achievable floor than daily grain
  // on this intermittent-demand catalog, so the headline uses its own thresholds.
  if (wmape === null) return "text-muted-foreground"
  if (wmape <= 60) return "text-green-600"
  if (wmape <= 90) return "text-yellow-600"
  return "text-red-600"
 }
 function formatSignedPct(ratio: number | null, digits = 0): string {
  // ratio is a fraction (0.7 => +70%); null-safe.
  if (ratio === null || ratio === undefined) return "N/A"
  const pct = ratio * 100
  const sign = pct > 0 ? "+" : ""
  return `${sign}${pct.toFixed(digits)}%`
 }
 export function ForecastAccuracy() {
  const { data, error, isLoading } = useQuery<AccuracyData>({
    queryKey: ["forecast-accuracy"],
@@ -133,6 +158,24 @@ export function ForecastAccuracy() {
    sampleSize: lt.sampleSize,
  }))
  // Headline prefers the weekly-grain WMAPE (informative); falls back to the
  // daily-grain number until enough complete weeks of history exist.
  const weeklyWmape = data?.overallWeekly?.wmape ?? null
  const usingWeekly = weeklyWmape !== null
  const headlineWmape = usingWeekly ? weeklyWmape : (data?.overall?.wmape ?? null)
  const headlineColor = usingWeekly
    ? getWeeklyAccuracyColor(headlineWmape)
    : getAccuracyColor(headlineWmape)
  // Net forecast-vs-actual ratio (e.g. +70% = over-forecasting), from the
  // daily 'all' totals — far more legible than bias in raw units.
  const totalFc = data?.overall?.totalForecast ?? 0
  const totalAct = data?.overall?.totalActual ?? 0
  const fcVsAct = totalAct > 0 ? (totalFc / totalAct - 1) : null
  // Value over the naive baseline; prefer weekly grain to match the headline.
  const naiveSource = data?.overallWeekly ?? data?.overall
  const naiveWmape = naiveSource?.naiveWmape ?? null
  const fva = naiveSource?.fva ?? null
  return (
    <div>
      <h3 className="text-lg font-medium mb-3">Forecast Accuracy</h3>
@@ -148,10 +191,24 @@ export function ForecastAccuracy() {
              <div className="flex items-baseline justify-between">
                <div className="flex items-center gap-2">
                  <Target className="h-4 w-4 text-muted-foreground" />
-                  <p className="text-sm font-medium text-muted-foreground">WMAPE</p>
+                  <p className="text-sm font-medium text-muted-foreground">
                    WMAPE <span className="text-[10px] opacity-70">({usingWeekly ? "weekly" : "daily"})</span>
                  </p>
                </div>
-                <p className={`text-lg font-bold ${getAccuracyColor(data?.overall?.wmape ?? null)}`}>
+                <p className={`text-lg font-bold ${headlineColor}`}>
-                  {formatWmape(data?.overall?.wmape ?? null)}
+                  {formatWmape(headlineWmape)}
                </p>
              </div>
              <div className="flex items-baseline justify-between">
                <div className="flex items-center gap-2">
                  <ArrowUpDown className="h-4 w-4 text-muted-foreground" />
                  <p className="text-sm font-medium text-muted-foreground">Forecast vs actual</p>
                </div>
                <p className="text-lg font-bold">
                  {formatSignedPct(fcVsAct)}
                  <span className="text-xs font-normal text-muted-foreground ml-1">
                    {(fcVsAct ?? 0) > 0 ? "over" : (fcVsAct ?? 0) < 0 ? "under" : ""}
                  </span>
                </p>
              </div>
              <div className="flex items-baseline justify-between">
@@ -160,20 +217,24 @@ export function ForecastAccuracy() {
                  <p className="text-sm font-medium text-muted-foreground">MAE</p>
                </div>
                <p className="text-lg font-bold">
-                  {data?.overall?.mae !== null ? data?.overall?.mae?.toFixed(2) : "N/A"}
+                  {data?.overall?.mae != null ? data?.overall?.mae?.toFixed(2) : "N/A"}
                  <span className="text-xs font-normal text-muted-foreground ml-1">units</span>
                </p>
              </div>
              <div className="flex items-baseline justify-between">
                <div className="flex items-center gap-2">
-                  <ArrowUpDown className="h-4 w-4 text-muted-foreground" />
+                  <Swords className="h-4 w-4 text-muted-foreground" />
-                  <p className="text-sm font-medium text-muted-foreground">Bias</p>
+                  <p className="text-sm font-medium text-muted-foreground">vs naive</p>
                </div>
                <p className="text-lg font-bold">
-                  {formatBias(data?.overall?.bias ?? null)}
+                  <span className={fva != null ? (fva > 0 ? "text-green-600" : "text-red-600") : "text-muted-foreground"}>
-                  <span className="text-xs font-normal text-muted-foreground ml-1">
+                    {fva != null ? `${formatSignedPct(fva)} FVA` : "N/A"}
                    {(data?.overall?.bias ?? 0) > 0 ? "over" : (data?.overall?.bias ?? 0) < 0 ? "under" : ""}
                  </span>
                  {naiveWmape != null && (
                    <span className="text-xs font-normal text-muted-foreground ml-1">
                      naive {formatWmape(naiveWmape)}
                    </span>
                  )}
                </p>
              </div>
            </div>