090°Open data
Open datasets, fully documented — searchable here, and readable by any LLM.
1 datasets
US retail seasonal demand index (derived)
Who joins this: an online shop joins monthly revenue on (category_code, calendar_month) and divides out seasonal_index to separate real performance from calendar seasonality; a sales team joins pipeline value on calendar_month for the same deseasonalizing. Method: the 12 Census Monthly Retail Trade Survey kind-of-business series (NAICS 441, 442, 443, 444, 445, 446, 447, 448, 451, 452, 453, 454) in NOT-seasonally-adjusted form via FRED's keyless fredgraph.csv (MRTSSM*USN, millions of dollars, 1992-01 ->). Classical multiplicative decomposition: centered 12-month moving average -> trend; seasonal-irregular ratio = value / trend; seasonal_index for each calendar month = median of that month's ratios across all complete years (median so one anomalous year cannot drag the profile), normalized so the 12 indices average exactly 1.0. si_stability_iqr is the interquartile range of the yearly ratios behind each point — the honest uncertainty. Category context: peak/trough month, seasonal amplitude, December lift, and median Q4 share of annual sales. 144 rows (12 categories x 12 months), refreshed annually once a new complete year lands. Caveats: the profile is a long-run national average per kind of business — structural shifts move it slowly; pandemic-2020 months are in the medians but outvoted by 30+ normal years; the trailing partial year is never used.
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