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new_generator and adds the two classes of dynamics that backbone
structurally cannot produce, plus a bilinear nonlinear-AR family. It is a
genuine capability upgrade — new mechanisms, not just re-weighted knobs.| file | purpose |
|---|---|
generator.py | class Generator(DataGenerator) — the entrypoint |
config.json | length band, SARIMA caps, core-family weights, enrichment probs |
requirements.txt | hash-locked, allowlisted deps (numpy, scipy) |
core_weights)SARIMA(p,d,q)(P,D,Q)_s with stochastic volatility, fat tails, integration.1/f^β colored-noise
spectral synthesis (β∈[-1,3]: anti-persistent → pink → Brownian), and
(ii) ARFIMA(p,d,0) via truncated fractional-differencing (Hosking)
coefficients. Fills the gap between SARIMA's I(0) and I(1) with genuine
long-range dependence (Hurst ≠ 0.5).y_t = φ y_{t-1} + b y_{t-1} e_{t-1} + θ e_{t-1} + e_t: bursty multiplicative autocorrelation distinct from
SETAR/GARCH.chaos_max_steps (default 1200) integration steps and then resample to the
target length, so cost stays linear and small even at length 4096.max_abs_value.d < 0.5 for stationarity.(seed, n_series) via
np.random.SeedSequence sub-seeds. The chaotic/bilinear scalar recursions are
ordinary deterministic float arithmetic. No hash(), wall-clock, or unseeded
global RNG.1# from the cascade repo root, with deps installed and cascade importable
2cascade verify ../generator_v2config.json)min_length / max_length — per-series length band.core_weights — probability mass over sarima / chaotic / longmem /
bilinear cores. SARIMA is kept dominant (strongest single scorer).chaos_min_steps / chaos_max_steps — chaotic integration-step budget
(bounds cost; trajectories are resampled to the series length).max_ar / max_ma / max_seasonal_ar / max_seasonal_ma — SARIMA caps.d_weights / seasonal_d_weights — integration-order probability mass.seasonal_prob / student_t_prob / stoch_vol_prob — SARIMA innovations.regime_prob / max_regimes — regime-switching frequency and count.trend_prob / calendar_prob / level_shift_prob / spike_prob — components.warp_prob / mixup_prob — nonlinear marginal warp / TSMixup probabilities.standardize — z-normalise each series (default false).max_abs_value — hard magnitude clip to keep output trainer-safe finite.