Document the final stability fixes and the model reset

README: window states and forecast assumptions, Model Error incomplete and
skipped spans, Forecast Error classes and why 2 and 5 minutes, and the
lifecycle tests. docs/MODEL.md: strict inputs, unknown-window assumptions,
the plan-matching table. CHANGELOG gains an Unreleased section (version
unchanged); ROADMAP and AUDIT record the findings, root causes, evidence and
remaining limitations.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

ROADMAP.md

@@ -95,6 +95,18 @@ changed during optimization or by an overtaken run; and model accuracy reported
 as if it were forecast accuracy. Reload/unload tests now cover repeated reloads
 (one relay listener, one polling coordinator). Released as 0.4.1; not yet tested on HAOS.
 
+### Final stability fixes after 0.4.1 — 2026-10-27
+
+An independent review of 0.4.1 reported four problems; three were reproduced and
+the fourth (lifecycle races) confirmed by reading Home Assistant's unload order.
+Fixed with regression tests (see AUDIT.md): Model Error spans with unknown inputs
+are now `incomplete`, not scored; window sensors unavailable at startup are
+unknown, with last known states saved and restored as unknown; unload, reload
+and removal can no longer publish stale results or recreate a deleted model;
+Forecast Error scores only forecasts whose plan the relay matched within 2
+minutes. Also added: **Reset learned model** in Configure. Not released or tested
+on HAOS yet.
+
 ## Milestone 1 — Explainable forecasts and dashboards (target v0.3.0)
 
 **Objective:** Users can verify what the model predicts and whether it is actually accurate.
@@ -103,7 +115,7 @@ as if it were forecast accuracy. Reload/unload tests now cover repeated reloads
 - [x] Expose predicted +15/+30/+60/+120 minute temperatures and full proposed ON/OFF schedule. *(0.3.0: `forecast_15min` and `plan_15min` on each room forecast entity.)*
 - [ ] Provide an HA dashboard example using native cards or a clearly documented optional chart card (do not bundle a heavyweight frontend unnecessarily).
 - [ ] Make clear that the **MPC plan forecast is counterfactual** while Generic Thermostat is controlling the relay; add a separate actual-policy forecast/evaluation pathway. *(0.3.0: plan forecasts labelled counterfactual; evaluation uses the actual relay history. Stabilization: published forecasts are scored only when the relay followed their plan and otherwise counted as `counterfactual`. An actual-policy forecast, simulating Generic Thermostat, is still open.)*
-- [ ] Store a bounded history of predictions with snapshots of inputs, proposed plan and eventually observed outcome. *(Stabilization: each published forecast is kept with its start readings, plan, model revision and assumptions until scored, in memory only; the scored statistics persist. No browsable history yet.)*
+- [ ] Store a bounded history of predictions with snapshots of inputs, proposed plan and eventually observed outcome. *(Stabilization: each published forecast is kept with its start readings, plan, model revision and assumptions, including any it had to assume, until scored, in memory only; the scored statistics persist, split into matched, approximate, counterfactual and incomplete. No browsable history yet.)*
 - [x] Implement out-of-sample/walk-forward MAE and bias at 15, 30, 60 and 120 minutes; include sample counts and confidence/quality labels. *(0.3.0: replay with parameters frozen at the forecast start and the actual relay history; `skill` against persistence. Stabilization: these are now the Model Error sensors, with RMSE, effective sample counts and quality labels; a separate Forecast Error sensor scores published forecasts. No confidence intervals yet.)*
 - [ ] Compare against simple benchmarks such as current-temperature persistence and a fixed thermal baseline. *(0.3.0: persistence done; fixed baseline open.)*
 - [ ] Add inspectable explanations: expected heat loss, expected heating response, target, room weights and reason for the next proposed action.
@@ -187,9 +199,12 @@ Items 1–3 of the original backlog are done (see AUDIT.md and CHANGELOG.md 0.2.
 3. Configure zones, the UPS heat source and the windows on HAOS; after a week of
    heating review Model Error `improvement_vs_base` and `quality`, per-zone bias,
    coefficient `confidence` (base layer included), `model_changes` after any
-   reconfiguration, and the Forecast Error counts (how many forecasts were scored
-   vs counterfactual). Switch off optional terms that do not help. Also try an
-   unavailable window sensor and a zone edit during HAOS acceptance.
+   reconfiguration, and the Forecast Error counts (matched, approximate,
+   counterfactual, incomplete and why). Switch off optional terms that do not
+   help. During HAOS acceptance also try: a window sensor unavailable across an HA
+   restart, a zone edit, a reload while the integration is running, *Reset learned
+   model* and removing a disposable entry (its `.storage/heating_mpc.<entry_id>`
+   file must be gone afterwards).
 4. Milestone 1 (rest): a dashboard example; forecast timestamps/provenance;
    explanations for the proposed action; an actual-policy forecast that simulates
    Generic Thermostat.