Update docs with latest architecture and results

docs/README.md

@@ -16,3 +16,8 @@ Tools:
 - `example/run_all_full.py` for one-command full pipeline + diagnostics.
 Notes:
 - If `use_quantile_transform` is enabled, run `prepare_data.py` with `full_stats: true` to build quantile tables.
+
+Current status (high level):
+- Two-stage pipeline (GRU trend + diffusion residuals).
+- Quantile transform + post-hoc calibration enabled for continuous features.
+- Latest metrics (2026-01-27 21:22): avg_ks ~0.405 / avg_jsd ~0.038 / avg_lag1_diff ~0.145.

docs/decisions.md

@@ -71,3 +71,10 @@
   - `example/export_samples.py`
   - `example/prepare_data.py`
   - `example/config.json`
+
+## 2026-01-27 — Full quantile stats in preparation
+- **Decision**: Enable full statistics when quantile transform is active.
+- **Why**: Stabilize quantile tables and reduce CDF mismatch.
+- **Files**:
+  - `example/prepare_data.py`
+  - `example/config.json`

docs/experiments.md

@@ -27,3 +27,8 @@ YYYY-MM-DD
 - Config: `example/config.json` (two-stage residual diffusion; user run on Windows)
 - Result: 0.7096230 / 0.0331810 / 0.1898416
 - Notes: slight KS improvement, lag-1 improves; still distribution/temporal trade-off.
+
+## 2026-01-27
+- Config: `example/config.json` (quantile transform + calibration, full stats)
+- Result: 0.4046 / 0.0376 / 0.1449
+- Notes: KS and lag-1 improved significantly; JSD regressed vs best discrete run.

docs/ideas.md

@@ -11,3 +11,6 @@
 
 ## Two-stage training with curriculum
 - Hypothesis: train diffusion on residuals only after temporal GRU converges to low error.
+
+## Discrete calibration
+- Hypothesis: post-hoc calibration on discrete marginals can reduce JSD without harming KS.