zenfro_v2 — Grammar of Time on the v1 weather backbone

Cascade miner generator. Evolves zenfro_v1 (weather-heavy aurora blend) with Grammar of Time (GoT) and forecast-friendly seasonality upgrades.

Why this prior

Cascade holds Toto2 fixed and trains it from scratch on your corpus. Eval is forecast skill on held-out windows: 4096-step context → next 64 steps (CRPS + MASE). Winning synthetic priors (Chronos-2, TempoPFN, CauKer) compose temporal primitives rather than sampling one ARIMA family.

Layer	What
Stems (v1)	weather (~34%), trend/seasonal, AR2, integrated, RFF-GP, …
GoT (~22%)	compose ⊕, splice ‖, nested ⋉, causal ▷
Seasonality	coupled calendar pairs (24↔168, 7↔52, …) + amp/phase drift
Clean mode	low-noise seasonal rows for sharp periodic reconstruction

Grammar of Time

Series     → Production | Stem
Production → Compose | Splice | Nested | Causal
Compose    → Stem ⊕ Stem [⊕ Stem]     # additive / multiplicative phrase
Splice     → Stem ‖ Stem              # clause = change of generating law
Nested     → Envelope ⋉ Carrier       # slow modulates fast
Causal     → Driver ▷ Response        # lagged temporal chain

GoT stems are cheap (seasonal+AR, RFF, integrated, AR2, diurnal-weatherish) so compositions stay wall-safe under the token budget.

Layout

zenfro_v2/
  generator.py      # Generator(DataGenerator)
  config.json       # active mixture + GoT knobs
  requirements.txt  # numpy + numba
  README.md

Quick checks

cascade verify ./mywork/zenfro_v2
cascade score ./mywork/zenfro_v2 --pool-dir <held-out>

Config knobs

key	role
family_weights	mixture over stems + GoT
got_depth	stems stacked in compose (2–4)
got_mul_frac	multiplicative vs additive compose
got_splice_cuts	clause boundaries in splice
got_nest_ratio	slow/fast scale span
got_causal_lag_frac	max lag as fraction of L
sa_clean_*	clean low-noise seasonal mode
tr_* / gr_*	trend / growth excursion

Contract

• Deterministic at a fixed seed
• NumPy + numba only, code-only (no shipped weights)
• Full-context series (min_length = max_length = 4096)