forecasting/bootstrap/SCHEMA.md

Forecasting bootstrap reference — schema

The Track-3 bootstrap reference is two files:

forecasting/bootstrap/draws.parquet      # zstd
forecasting/bootstrap/draws.meta.json    # provenance sidecar

draws.parquet

One row per (reduction, model, scope, metric, draw) — a single long frame holding the per-draw values for all three reductions.

column	type	description
reduction	string (dict)	which reduction the row belongs to: skill, rank, or fairness
model	string (dict)	method identifier (10 values; see draws.meta.json:methods)
scope	string (dict)	the headline scope (see below)
metric	string (dict)	scored metric for rank rows (mae / auroc / overall); empty "" for skill and fairness
draw	int32	bootstrap-draw index in [0, n_boot)
value	float32	the per-draw value of this reduction for (model, scope[, metric])

scope values by reduction

• skill: channel_0_score..channel_18_score, sleep_score, workout_score, activity_score, physiology_score, overall_score.
• rank: channel_<i>, sleep, workout, activity, physiology, overall (paired with metric ∈ mae / auroc / overall).
• fairness: age_group, sex, overall, the 4 sensor categories (activity / physiology / sleep / workout), and channel_<i>.

value semantics

• skill — paired skill score 1 − exp(mean_task log R) vs seasonal_naive, per draw (resampled-user cohort). R is the clipped per-task error ratio; continuous error = MAE, binary error = max(1 − AUROC, 0.005).
• rank — cross-method average rank for the draw (lower error → rank 1), meaned over the resampled cohort.
• fairness — MAPD disparity-ratio fair skill score for the draw: per-task disparity D = mean(|E_g − E_g'|) over subgroup pairs, ratio vs the baseline's D, clipped, geomean-averaged across tasks (category-balanced), macro-averaged across attributes.

Resamples are paired across methods (one shared boot_idx matrix, seed=42), so per-draw cross-method comparisons (skill ratios, ranks) are valid.

draws.meta.json

{
  "n_boot": 1000,
  "seed": 42,
  "ci_level": 0.95,
  "splits": ["test"],
  "baseline": "seasonal_naive",
  "methods": ["seasonal_naive", "autoARIMA", ...],   // 10 entries
  "continuous_metrics": ["mae"],
  "binary_metrics": ["auroc"],
  "age_bins": [18, 30, 40, 50, 60],
  "reductions": ["skill", "rank", "fairness"],
  "within_user_aggregation": "micro",
  "aggregation_unit": "user",
  "n_rows": 0,
  "git_commit": "...",
  "timestamp": "..."
}

Conventions

• Evaluated against the canonical split sharable_users_seed42_2026 (test).
• Track-3 baseline for skill / fairness: seasonal_naive.
• Fairness disparity primitive: MAPD (mean absolute pairwise difference); for a 2-level attribute (sex) this equals the historical max-min.
• Format: single Parquet, dictionary-encoded categoricals, float32 value, int32 draw index, zstd compression.

Tracks

dir	track	status
imputation/bootstrap/	Track 2 — Imputation	live
forecasting/bootstrap/	Track 3 — Forecasting (above)	live
downstream/bootstrap/	Track 1 — Outcome Prediction	added later