cascade-fullctx-hybrid-v19

A differentiated NumPy/SciPy full-context generator derived from the public custom-fullctx-v4 design.

Version 19 combines the strongest transferable parts of three matched full-wall checkpoints: cascade9's spectral/long-memory laws, cascade-v16's causal sensor/count/event refinements, and the evidence from cascade-v17 that a 4% bounded-regime substitution is not broadly beneficial. It also retains the targeted family introduced in v18 from the public j-test/dasadas_v18: period-7 non-negative demand with weekday profiles, weekend dips, slow trend, random-walk level, promotions with echoes, holiday dips, and a Poisson-count branch.

The external generator measured an 8% demand weight as its exact sweet spot on a multi-domain pool (+6.39% paired-cluster-bootstrap LCB); 10% overshot and failed. This integration keeps that 8% family but does not copy the older external base. Cascade-v16's OU, physical-sensor, stateful count/intermittent, and conditional-pulse families retain their relative composition, scaled proportionally into the remaining 92%.

The GP and long-memory sentence above describes the inherited v16 scaffold. V19 intentionally swaps those two builders back to cascade9's simpler L-point RBF and broad spectral 1/f^beta implementations because cascade9 beat v16 on the nature-heavy revealed pool despite identical family weights and less realized compute. All other v16 safeguards remain. See ANALYSIS.md for the domain and compute decomposition.

The refinement retains hard subtypes rather than optimizing for an easy synthetic self-score: integrated paths keep genuine I(1) and I(2) branches while placing more mass on learnable persistent velocity, and 20% of OU rows retain an ultra-slow near-unit-root rate while 15% retain rapid hidden switching.

Design

• Emits only 4096-point series, matching the 128-patch training/eval geometry and minimizing the one non-target context patch paid per series.
• Samples trend as total end-to-end excursion, so trend strength is independent of series length.
• Mixes fifteen families: trend/seasonality, structural regimes, multiplicative series, AR/integrated/nonlinear dynamics, smooth spectral GP, power-law long memory, physical sensors, seasonal counts, intermittent demand, outliers, and dedicated weekly non-negative demand.
• Adds an 8% period-7 demand family with randomized weekday phase/profile, optional two-day weekend depression, slow level evolution, sparse promotions and holiday dips, and a 35% Poisson-count branch.
• Replaces the predecessor's slow 48-pass random-Fourier GP with one batched inverse FFT and adds persistent/anti-persistent spectral paths.
• Adds a small regime-switching mean-reverting family with bounded clustered volatility, heavy-tailed innovations, transient shocks, and seasonal means.
• Executes the OU recurrence with SciPy's compiled linear filter instead of a 4095-step Python loop.
• Executes AR(1) and AR(2) recurrences with compiled SciPy filters; local microbenchmarks were 4–12× faster than scanning time in Python.
• Adds batched cadence-seasonal Poisson and gamma-mixed Poisson counts with signed trends and decaying bursts, preserving positive integer structure.
• Adds weekday/weekend interactions to a minority of count rows and piecewise spectral slopes to long-memory rows, covering calendar effects and scale-dependent roughness without another FFT.
• Gives 40% of trend-seasonal rows a low-innovation mode, teaching sharp, stable periodic reconstruction while retaining noisy seasonal coverage.
• Anchors most mixture mass on the five-seed-tested full-context core (trend/seasonal, regimes, multiplicative, AR2, and integrated paths), while retaining each newer prior at a conservative share.
• Uses published TempoPFN ablations to strengthen OU/SDE-like dynamics, spectral/long-memory paths, and transient events without letting one family dominate the corpus.
• Adds slowly modulated amplitude and phase to a minority of seasonal components; stationary cycles remain the majority. Multiplicative paths use the same full cadence bank instead of a four-period subset.
• Extends structural/event coverage with piecewise-affine regime trends, decaying shock recovery, event plateaus, and genuine held-constant runs.
• Keeps independent outliers as only 15% of the 1%-weight pulse family; most pulse rows instead use jittered cadence, seasonal event intensity, or a stable Hawkes-like recurrence whose near-future hazard responds to history.
• Applies low-rate causal censoring, finite-range quantization, and sample-and-hold artifacts to bridge clean priors to real measurement pipelines. Time reversal is limited to laws valid under reversal.
• Extends seasonality through 365/672/730-step cycles and adds a small generic physical-sensor family (smooth, bounded, pressure-like, and skewed-positive) without adopting the competitor's private-pool-shaped weather weighting.
• Generates lazy 2048-row random-family chunks, keeping every stream prefix mixed while amortizing Python dispatch. Local profiling found this about 6% faster than 1024 rows; 4096 rows regressed slightly.
• Evaluates optional seasonal components only for active rows while preserving the fixed RNG draw sequence, and caches the fixed cadence sine/cosine basis, reducing trigonometric work without narrowing the prior.
• Draws jump, shock, and heavy-tail values only where those sparse branches are active instead of allocating dense arrays whose values are mostly discarded.
• Corrects the AR(2) family to remain stationary instead of adding a 20-unit full-context drift, and scales I(2) paths by L rather than sqrt(L) so they no longer dominate I(1) paths.
• Burns in stationary AR(2) and SETAR recurrences rather than emitting their arbitrary zero-state transient.
• Calibrates standardization and measurement thresholds on the first 512 observations, preventing generated history from depending on unseen targets.
• Excludes hard range artifacts from integrated paths so an unbounded process cannot become an artificial absorbing plateau. Sign inversion and causal sample-and-hold remain available.
• Uses ForecastPFN-style expectation-centered Weibull multiplicative noise, chaotic-map burn-in with optional observation noise, and ±5% period jitter on slowly evolving seasonal components.
• Uses cascade9's empirically stronger L-point RBF spectral sampler.
• Uses cascade9's broad anti-persistent-to-persistent 1/f^beta law with a 25% integrated branch and piecewise spectral slopes.
• Replaces permanent volatility jumps with bounded mean-reverting log stochastic volatility calibrated to TempoPFN's OU ranges.
• Makes intermittent demand genuinely zero-inflated with a correlated occurrence state and positive integer sizes. Count artifacts preserve integer values and causal direction.

The v13 predecessor generated a five-run median of 6.59M points/s versus 7.23M for the archived v12 baseline. The exact composite covariance and richer observation models cost about 8.8% throughput, but the candidate remains 78% above the mainnet contract's 3.7M reference in isolation. End-to-end token completion also includes model training and stream handoff. Re-measure v19 before treating those inherited numbers as current.

An end-to-end isolation run found that synchronous generation left training blocked on data for 21.9% of its wall (2.13M point-passes/s). A deterministic one-chunk producer thread now overlaps NumPy/SciPy generation with GPU work, cutting data wait to 3.9% and raising training throughput to 2.43M point-passes/s (+14.4%) on the A100. The same short contract budget then completed without a deadline hit. Cached rows reached 2.74M, confirming the remaining gap to the live L40S reference is mostly model/device throughput.

A controlled 120-second parameter screen then compared the baseline mixture with seasonal-, spectral-, and dynamics-heavy variants under the same model, pool, budget, and seeds. Dynamics-heavy won all three validation seeds, reducing mean local synthetic-pool geomean from 0.19097 to 0.18431 (3.5%; lower is better). The applied weights increase AR(2), integrated, threshold-AR, chaotic, regime-shift, and OU coverage while reducing stationary seasonal, spectral, and sparse/count families. This remains a directional local result, not a live validator verdict.

Local training result

V19 has not yet completed a full matched train. The external 8% family result is strong evidence for the process and weight on its own base, not proof that the same gain transfers unchanged to this hybrid. Run a matched-seed screen against cascade9 before deployment.

The v10 corpus was trained under the mainnet chain.toml contract on an A100 for the full 3-hour wall. It scored 0.13679 on the 64-window local synthetic smoke pool (lower is better), improving from 0.15429 at the 30-minute heat budget, while reaching 55% of the token budget. The optimized dynamics-heavy v11 heat reached 59% (3.90B / 6.66B) and scored 0.15424. The v12 prefetch isolation test then cut data wait from 21.9% to 3.9% and raised end-to-end throughput from 2.13M to 2.43M point-passes/s. These scores are directional and are not live-validator verdicts; the A100 remains below the contract's L40S-calibrated 3.7M reference.

Research basis

• TempoPFN supplies the regime-switching OU ranges, seasonal jitter, bounded stochastic-volatility rationale, and evidence for diverse step/spike priors: https://arxiv.org/html/2510.25502v1.
• ForecastPFN motivates multiplicative Weibull noise whose center does not bias the underlying signal: https://arxiv.org/abs/2311.01933.
• Chronos KernelSynth motivates RBF and Rational-Quadratic covariance composition: https://arxiv.org/html/2403.07815v1.
• Fractional-process literature gives beta=2H-1 for fGn and beta=2H+1 for fBm: https://pmc.ncbi.nlm.nih.gov/articles/PMC3947294/.
• Intermittent-demand state-space work separates occurrence probability from positive size: https://mpra.ub.uni-muenchen.de/82487/.
• Hawkes-process literature motivates a conditional event intensity increased by recent arrivals: https://arxiv.org/abs/2405.10527.
• Heavy-tail forecasting work motivates retaining unpredictable innovations for likelihood and tail calibration rather than exact timing prediction: https://arxiv.org/abs/2106.10952.
• Proper-scoring-rule theory motivates honest predictive distributions for irreducible event uncertainty: https://doi.org/10.1198/016214506000001437.

The trend, regime-step, SETAR, physical-sensor, seasonal-count, and pulse families retain their prior broad parameter ranges where no source establishes a universal Toto2-optimal distribution. Their implementations were checked for stability and structural validity; changing every number would be false precision. V19 scales the complete v16 mixture uniformly to fund the externally validated 8% weekly-demand prior.

Validate

python -m cascade.miner.cli verify ./generators/cascade-v19 --chain-toml chain.toml

Contract validity and CPU throughput do not establish forecasting quality. Run a production-faithful GPU A/B score against the current king before deploying this candidate.