Extend the read-only §9.6 rate-sensitivity OOS harness with two opt-in
candidate-method variants so any wiring decision is evidence-based:
- --almon: evaluate_oos_almon, Almon distributed-lag (regression.fit_almon_dl),
fit on TRAIN only, point-in-time sum_j beta_j*drate[t-j] prediction.
- --deseasonalize: train-only month-of-year factors (normalize.seasonal_factors)
divided out before log_diff, then the existing best_lag evaluator.
Both pin the fit to _time_ordered_split(n_train); no look-ahead leakage
(adversarial tests assert the train fit is byte-identical under test corruption).
Default path (best_lag/raw) is byte-identical to before. 88 tests pass, ruff clean.
Prod OOS findings (directional hit-rate, coin-flip 0.50, bar 0.55+lag-stable):
- #979 deseasonalize: neutral (B 0.148->0.148, A 0.40->0.40) -> keep advisory.
- #978 Almon-ADL: dominates best_lag (B 0.148->0.407 lag-stable; A 0.40->0.60,
clears coin-flip+margin) -> candidate to promote from advisory.
