090°Données ouvertes
Jeux de données ouverts, entièrement documentés — interrogeables ici, et lisibles par n’importe quel LLM.
Les titres et les descriptions proviennent des sources de données, en anglais.
1 jeux de données
US Retail Inventory-Cycle Intelligence (derived)
Monthly US retail inventory-cycle intelligence from the U.S. Census Bureau's Monthly Retail Trade Survey (official 'Inventories and Inventories/Sales Ratios' workbook: 9 published industries — retail trade total, ex-motor-vehicle total, motor vehicle and parts dealers (441), furniture/home-furnishings/electronics (4423X), building materials (444), food and beverage (445), clothing (448), general merchandise (452), department stores (4522); 1992-01 ->). Each industry-month row carries published inventory levels and inventories/sales ratios (seasonally adjusted and not adjusted, millions of USD / months of supply), inventory momentum (MoM, 3-month-annualized, YoY), the ratio's YoY change and 36-month z-score, a documented 0-100 inventory pressure score (50% winsorized ratio z-score +-2.5 + 30% winsorized inventory YoY +-25pp + 20% winsorized ratio YoY change +-1.0pp, min-maxed within each month) with deterministic rank and p1-p4 tiers across the 7 peer industries, overstock/understock flags (|z| > 1.0), 36-month ratio record-high and inventory-surge flags, the exact unpublished-kinds inventory residual on the ex-441 row, plus month-level context (total and ex-441 ratios, overstock/understock breadth, the month's top pressure industry, median pressure score). An online shop joins monthly inventory pressure to its own stock planning on (date, industry_code) for overstock/understock and markdown timing; a sales team joins category pressure to pipeline value on date. Method: one polite GET of the official Census workbook; a trailing-36-month verbatim transcription is embedded for offline safety; the transform is pure pandas in map_schema with no imputation (warmup cells are honest nulls). Caveats: the latest month is preliminary and revised next release; the published kind detail is not exhaustive (the unpublished residual covers 446/447/451/453/454); the 2020-04 ratios for clothing (18.63) and department stores (48.93 SA / 51.88 NSA) reflect the COVID sales collapse, not a data error; department stores (4522) are a subset of general merchandise (452) and are excluded from the additive identity.
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