{
  "schema_version": "1.0",
  "study_id": "S02",
  "run_id": "S02-5145fea6-e598f30c",
  "code_version": "5145fea621938bb3455655a685897dfa69a2cd83",
  "source_sha256": {
    "research_program/__init__.py": "5abb4829b477fd3068ee4c11dad2b59cd33e90aad06a17a5e9fb2f840a75abf1",
    "research_program/evaluation.py": "09a5d060c34b8cd1e5a3330d90f5ff3e12adbca8cfe1ebe3297da2b91d227e45",
    "research_program/io.py": "16de123c16d80bce5eb8d6766224dec1729d7c00449c2528f2348d86ad68d35f",
    "research_program/validate.py": "3e6a591e2b77799bdf73683db438b2998e2e457340626c98b7ceed510ab3329e",
    "studies/S02/config.json": "e7be7c23ef521795de1ac104de08e232206ce783ea29fda86be166e28cf96499",
    "studies/S02/forecast.py": "f6985b6bbc528b9aab8eb2282f9a19281299f0501b6097740499dcda2f0c1833",
    "studies/S02/ingest.py": "57b05dc632741a130251cf5116d367d1e4613d78a9a51b86fe04385db905f55d",
    "studies/S02/inventory.py": "78712519d6944ab8349c0b3a84c52deacd7aa7802576d954dac471202ff51fd9",
    "studies/S02/study.py": "29785d44a412a9e4000e3a8bbecbc852ec9193b0e25e3fc51f955faf017ebc89",
    "uv.lock": "4d09c3bedbc7dc0c73b35263db97de40564730f5a0ec9752595ddf4e02c018fd"
  },
  "evaluated_on": "2026-09-29",
  "data": {
    "source_url": "https://github.com/Mcompetitions/M5-methods",
    "release": "Organizer evaluation release; files inspected 2026-09-29; sales through d_1969",
    "sha256": "e598f30c3803f6ac04b9d7cc7ac7775e755b44627b30a58278a791e34a80ea11",
    "file_hashes": {
      "calendar.csv": {
        "sha256": "568d0fe5f41790142379698732908e4e57432c1c6396f3f59fb880a9c2b54231",
        "bytes": 112477,
        "url": "https://drive.google.com/file/d/1eeU8Me44yzgWsDD0dYrX4xVOixil_H4u/view"
      },
      "sales_train_evaluation.csv": {
        "sha256": "c21a519596680feb86f27a9e62f6c8b583f8be60c2c195f080ae8ca2990af2b7",
        "bytes": 121168898,
        "url": "https://drive.google.com/file/d/1-khP8tp1gRTfaQQV_PlKd6R_3Mw5KiBw/view"
      },
      "sales_test_evaluation.csv": {
        "sha256": "73773250dd7f7464f7a1bf3a54874dc7fc8cbb49885cd0bf1efe09bf48fd930d",
        "bytes": 3260569,
        "url": "https://drive.google.com/file/d/1Bj7Xj15yn4j6-BM_mpSmOVTV0c2ihEdo/view"
      },
      "sell_prices.csv": {
        "sha256": "5c7cf045b1abbdc8d5007809c40b3682c5216075937f93e2cef01caddc4a30d9",
        "bytes": 237073689,
        "url": "https://drive.google.com/file/d/1YKo2D7rpg9y5L-qaidUjyJaKvTIR0rMI/view"
      }
    },
    "license": "Publisher competition distribution; raw data not redistributed; no additional rights asserted.",
    "citation": "Makridakis, Spiliotis & Assimakopoulos (2022), M5 accuracy competition; official M5-methods repository and competitors guide.",
    "coverage": "2011-01-29–2016-06-19; 210 selected product/store series"
  },
  "target": {
    "label": "Next 28-day recorded unit-sales total",
    "estimand": "Sales forecast for a fixed historically available assortment, not latent demand",
    "prediction_cutoff": "Origin-day close; only already completed weekly prices",
    "horizon": "28 days",
    "unit": "product/store replenishment cycle"
  },
  "cohort": "21 deterministically selected items across all ten stores; four non-overlapping final 28-day target windows ending June 19, 2016.",
  "samples": {
    "total": 413490,
    "test": 840,
    "series": 210,
    "items": 21,
    "stores": 10,
    "origins": 4,
    "days_per_horizon": 28,
    "tuning_examples": 21210
  },
  "models": [
    {
      "id": "seasonal_7",
      "label": "Repeat the last week",
      "specification": {
        "horizon": 28,
        "chosen_leaves": null,
        "quantiles": [
          0.1,
          0.5,
          0.9
        ],
        "scenario_draws": 256
      }
    },
    {
      "id": "seasonal_28",
      "label": "Repeat the last cycle",
      "specification": {
        "horizon": 28,
        "chosen_leaves": null,
        "quantiles": [
          0.1,
          0.5,
          0.9
        ],
        "scenario_draws": 256
      }
    },
    {
      "id": "quantile_boosting",
      "label": "Global quantile boosting",
      "specification": {
        "horizon": 28,
        "chosen_leaves": 15,
        "quantiles": [
          0.1,
          0.5,
          0.9
        ],
        "scenario_draws": 256
      }
    }
  ],
  "metrics": [
    {
      "name": "weighted_scaled_error_28d",
      "model": "seasonal_7",
      "estimate": 1.2264603,
      "unit": "scaled cycle-total error",
      "split": "four final rolling origins",
      "lower": 1.0864475,
      "upper": 1.42688178,
      "confidence": 0.95
    },
    {
      "name": "scaled_pinball",
      "model": "seasonal_7",
      "estimate": 1.32261396,
      "unit": "scaled pinball loss",
      "split": "four final rolling origins",
      "lower": 1.08140764,
      "upper": 1.6571945,
      "confidence": 0.95
    },
    {
      "name": "coverage_80",
      "model": "seasonal_7",
      "estimate": 0.74642857,
      "unit": "fraction",
      "split": "four final rolling origins",
      "lower": 0.70833333,
      "upper": 0.78452381,
      "confidence": 0.95
    },
    {
      "name": "weighted_scaled_error_28d",
      "model": "seasonal_28",
      "estimate": 0.97459984,
      "unit": "scaled cycle-total error",
      "split": "four final rolling origins",
      "lower": 0.8079415,
      "upper": 1.11506266,
      "confidence": 0.95
    },
    {
      "name": "scaled_pinball",
      "model": "seasonal_28",
      "estimate": 1.21859767,
      "unit": "scaled pinball loss",
      "split": "four final rolling origins",
      "lower": 0.85636909,
      "upper": 1.55249401,
      "confidence": 0.95
    },
    {
      "name": "coverage_80",
      "model": "seasonal_28",
      "estimate": 0.76428571,
      "unit": "fraction",
      "split": "four final rolling origins",
      "lower": 0.71541667,
      "upper": 0.81434524,
      "confidence": 0.95
    },
    {
      "name": "weighted_scaled_error_28d",
      "model": "quantile_boosting",
      "estimate": 0.80488671,
      "unit": "scaled cycle-total error",
      "split": "four final rolling origins",
      "lower": 0.69353645,
      "upper": 0.8981696,
      "confidence": 0.95
    },
    {
      "name": "scaled_pinball",
      "model": "quantile_boosting",
      "estimate": 1.11868389,
      "unit": "scaled pinball loss",
      "split": "four final rolling origins",
      "lower": 0.78577284,
      "upper": 1.42227841,
      "confidence": 0.95
    },
    {
      "name": "coverage_80",
      "model": "quantile_boosting",
      "estimate": 0.88333333,
      "unit": "fraction",
      "split": "four final rolling origins",
      "lower": 0.85,
      "upper": 0.91791667,
      "confidence": 0.95
    }
  ],
  "uncertainty": "500 paired item-cluster bootstrap draws retain all ten stores and four origins per item. Conditional on these locations, historical dates and fitted models. Scenario dependence uses 17 overlapping calibration windows, not 256 independent histories.",
  "assumptions": [
    "Recorded sales are used as scenario demand only; actual inventory availability is unknown.",
    "Price features and forecast weights use only completed source-calendar weeks.",
    "All economic costs and storage/lead-time settings are explicitly assumed.",
    "Aggregate scenarios sum bottom-level units exactly; marginal quantiles are not additive."
  ],
  "limitations": [
    "Bounded deterministic 21-item assortment; not a random sample of the full retail market.",
    "Cycle-total scaled error is not the official daily M5 leaderboard score.",
    "Only four final historical windows; item-bootstrap intervals do not quantify future time-regime uncertainty.",
    "Seventeen overlapping calibration windows limit tail and dependence estimation; forecast bands have no exact-coverage guarantee.",
    "No actual stock availability, retailer margin, purchase cost, spoilage or realized savings is observed.",
    "Independent technical review is pending."
  ],
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      "source_price_rows": 6841121,
      "selected_items": 21,
      "selected_series": 210,
      "selected_departments": 7,
      "stores": 10,
      "categories": 3,
      "calendar_start": "2011-01-29",
      "calendar_end": "2016-06-19",
      "zero_fraction_before_final": 0.71429597,
      "missing_price_at_final_origins": {
        "1857": 0,
        "1885": 0,
        "1913": 0,
        "1941": 0
      },
      "join_keys": [
        "item_id",
        "store_id"
      ],
      "price_cutoff": "Weekly price eligible only after its complete source-calendar week ends."
    },
    "tuning": [
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        "normalized_pinball": 0.60379448,
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        "crossing_rows_before_rearrangement": 1
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        "crossing_rows_before_rearrangement": 0
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    ],
    "selected_leaves": 15,
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      },
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    ],
    "primary_difference": {
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      "estimate": -0.16971313,
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    ],
    "scenario_units": "Assumed USD; holding per unit/day, lost-sale penalty per unmet unit.",
    "scenario_formula": "End-of-day inventory × holding cost + lost scenario units × penalty; one constrained order."
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  "software": {
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    "pandas": "2.3.3",
    "scipy": "1.17.1",
    "scikit-learn": "1.7.2"
  }
}
