US retail-sales signals
Monthly US retail-sales signals from Census advance retail data (FRED RSXFS nominal retail trade + RRSFS real retail & food services, 1992-01 ->, seasonally adjusted, keyless except the already-provisioned FRED key): year-on-year change, 3-month momentum, 12-month change volatility, 3-sigma anomaly flags, naive-drift forecasts, 5-year spending z-scores, and contraction/strong-expansion flags. The measured monthly household-spending lens between us-consumer-sentiment-signals (stated sentiment) and quarterly real PCE — the nominal/real split shows when spending growth is prices vs volume. All rows normalized to country_code USA. Raw data: Census Bureau via FRED.
Qualité
Attribution
US Census Bureau, Monthly Retail Trade Survey via FRED; derived signals by Frontier Data Hub
Schéma
| Colonne | Type | Description |
|---|---|---|
| date | string | Month of observation (Census Monthly Retail Trade Survey, monthly, seasonally adjusted). |
| country | string | |
| country_code | string | |
| series_id | string | RSXFS (advance retail sales, retail trade, nominal) or RRSFS (advance real retail and food services sales), FRED series IDs. |
| series_label | string | Series description: advance retail sales, retail trade (nominal, millions of dollars, SA) or advance real retail and food services sales (millions of 1982-84 CPI-adjusted dollars, SA). |
| value | float | Advance estimate of monthly retail sales in the series' native unit: nominal millions of dollars for RSXFS; millions of 1982-84 CPI-adjusted dollars for RRSFS. |
| yoy_change | float | |
| momentum_3m | float | |
| volatility_30d | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| spend_z_5y | float | |
| contraction_flag | integer | |
| strong_flag | integer |
Exemple de lignes
| date | country | country_code | series_id | series_label | value | yoy_change | momentum_3m | volatility_30d | anomaly_flag | forecast_1m | spend_z_5y | contraction_flag | strong_flag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1992-01-01 | United States | USA | RRSFS | Advance real retail and food services sales (millions of 1982-84 CPI-adjusted $, SA) | 115095 | — | — | — | 0 | — | — | 0 | 0 |
| 1992-02-01 | United States | USA | RRSFS | Advance real retail and food services sales (millions of 1982-84 CPI-adjusted $, SA) | 114855 | — | — | — | 0 | — | — | 0 | 0 |
| 1992-03-01 | United States | USA | RRSFS | Advance real retail and food services sales (millions of 1982-84 CPI-adjusted $, SA) | 114052 | — | — | — | 0 | — | — | 0 | 0 |
| 1992-04-01 | United States | USA | RRSFS | Advance real retail and food services sales (millions of 1982-84 CPI-adjusted $, SA) | 114721 | — | -0.32494895521091305 | — | 0 | — | — | 0 | 0 |
| 1992-05-01 | United States | USA | RRSFS | Advance real retail and food services sales (millions of 1982-84 CPI-adjusted $, SA) | 114868 | — | 0.011318619128464213 | — | 0 | — | — | 0 | 0 |
Télécharger un échantillon
Téléchargez l'échantillon complet de ce jeu de données (lignes d'exemple, pas le jeu complet).
Utiliser avec un LLM
Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).
cURL
curl "https://datazimuts.com/v1/datasets/retail_sales_signals/us_retail_sales_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/retail_sales_signals/us_retail_sales_signals").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)Point d’accès API: https://datazimuts.com/v1/datasets/retail_sales_signals/us_retail_sales_signals
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.