docs/benchmark-design.md

Benchmark Design

Design Goal

FUTURE-TS exists to make benchmark integrity a property of the evaluation process rather than a claim about careful dataset curation. The benchmark is designed around time of availability, frozen submissions, and explicit adaptation constraints.

The current codebase enforces this through:

• evaluator-issued run manifests (platform_issued_at, platform_received_at)
• deterministic prediction_hash and manifest_hash checks
• first-publication auditing on realized labels (first_published_at)

Tier Model

public_dev

Open-label tasks for development, debugging, and reproducible baselines. These tasks are fully inspectable and intentionally not sufficient for a final ranking.

blind_archive

Historical windows whose labels are hidden from participants. The evaluator can score them, but the submission contract still requires training and retrieval cutoffs to precede those labels.

live

Truly future windows. Predictions are submitted before outcomes are available. Scoring only becomes possible when the realized labels arrive.

Why Task Cards Matter

The benchmark does not rank model families against dataset names alone. It ranks behavior under declared operating conditions. A task card should answer:

• What is being predicted?
• At what issue time?
• Over what horizon?
• Which covariates are known, forecast, delayed, or unavailable?
• Is the task revision-aware?
• What is the latency and memory budget?
• Which metric actually matters in deployment?

Data Preparation Boundary

Core evaluation code should score benchmark artifacts; it should not depend on third-party API uptime. The empirical suite now keeps public-source fetchers under scripts/data_prep/, while future_ts.empirical_suite consumes cached source files and delegates cache misses to that data-prep module. This keeps runner/scorer tests isolated from FRED, NOAA, USGS, Melbourne, CDC, MTA, and NYC Open Data schema or rate-limit changes.

For strict benchmark releases, prefer materializing versioned task cards and actuals from static cache snapshots. Live API fetching is a data-prep step, not part of the scoring contract.

OOD Tagging

Each task carries OOD tags so evaluation can be sliced beyond domain labels. v0 uses a simple tag list, but the intended interpretation includes:

• time OOD
• entity OOD
• regime OOD
• structure OOD
• covariate OOD

Adaptation

Tasks declare which adaptation budgets are legal. The current scaffold assumes ordered budgets and reports:

• skill at each supported budget
• adaptation AUC across the supported budget path

This separates generalization from tunability.

Aggregation Philosophy

v0 computes:

• per-task raw metric
• anchor-normalized skill
• uncertainty and efficiency side metrics
• tier-level weighted means
• a capability vector
• a Pareto-friendly leaderboard summary

The decision-utility (D) dimension supports newsvendor-style tasks where a point forecast can be interpreted as an action. When quantile predictions are available, the expected holding/stockout metric uses the critical fractile p / (h + p) and scores the scaled pinball loss at that quantile. This keeps probabilistic submissions aligned with the decision that the user would actually make under asymmetric costs.

The robustness (R) dimension intentionally uses the least-adapted score for event or OOD-tagged tasks. Adapted performance still contributes to adaptation AUC (A), but it cannot dominate both R and A through the same improved few-shot budget.

The overall score is included for convenience, but the benchmark is intentionally not “lowest average error wins.”

The scorer also assumes comparable coverage. A report is only produced when the submission covers the full visible evaluation set for each task-budget pair represented in the realized labels. This avoids inflating overall score by skipping difficult tasks, series, or budgets.

What Is Still Not Implemented

• remote submission execution
• cryptographic signature verification against external key infrastructure
• wave-based ranking for cross-date comparability
• revision snapshots for inputs beyond realized labels
• large-scale hierarchical bootstrap over domains, datasets, and series

Those remain service concerns. This repo establishes and validates the local benchmark contract first.