US labor market signals (Sahm-rule recession indicator, claims anomalies, momentum, forecasts)

Name: US labor market signals (Sahm-rule recession indicator, claims anomalies, momentum, forecasts)
Creator: Labor Market Signals (derived)
License: https://fred.stlouisfed.org/legal
Keywords: labor, unemployment, jobless-claims, sahm-rule, recession, volatility, momentum, anomaly-detection, forecasting, signals, fred

Daily-row labor-market signals derived from FRED's US labor series: the Sahm-rule recession indicator on the unemployment rate (trigger flag + consecutive-month streak), 30-period annualized volatility of changes, 3-month momentum, 3-sigma anomaly flags, naive-drift 1-month forecasts, a per-date cross-series volatility rank, and the 4-week moving average on weekly jobless claims. Covers UNRATE (monthly unemployment rate), PAYEMS (monthly nonfarm payrolls), ICSA (weekly initial claims, seasonally adjusted) and CCSA (weekly continued claims, seasonally adjusted). All rows are normalized to country_code USA so they join cleanly with US macro data. Raw series: Federal Reserve Bank of St. Louis (FRED).

Source: Labor Market Signals (derived)8,224 lignesMis à jour: 22/09/2026

laborunemploymentjobless-claimssahm-rulerecessionvolatilitymomentumanomaly-detectionforecastingsignalsfred

Qualité

97.3

Attribution

Federal Reserve Bank of St. Louis (FRED; derived signals by Frontier Data Hub)

Schéma

Colonne	Type	Description
date	string	Observation date (FRED API field date; YYYY-MM-DD).
country	string
country_code	string
series_id	string	FRED series ID, e.g. UNRATE, PAYEMS, ICSA, CCSA; resolves to the series page at https://fred.stlouisfed.org/series/<id>.
series_label	string	Official FRED series title as published for the series.
value	float	Observation value as published by FRED for this series (unemployment rate in percent; nonfarm payrolls in thousands of persons; jobless claims in persons); see the series notes for methodology and revisions.
volatility_30d	float
momentum_3m	float
anomaly_flag	integer
forecast_1m	float
rank	integer
sahm_rule	float
sahm_trigger	integer
sahm_streak	integer
ma_4w	float

Exemple de lignes

date	country	country_code	series_id	series_label	value	volatility_30d	momentum_3m	forecast_1m	rank	sahm_rule	ma_4w
1939-01-01	United States	USA	PAYEMS	All Employees, Total Nonfarm	29923	—	—	—	—	—	—
1939-02-01	United States	USA	PAYEMS	All Employees, Total Nonfarm	30100	—	—	—	—	—	—
1939-03-01	United States	USA	PAYEMS	All Employees, Total Nonfarm	30280	—	—	—	—	—	—
1939-04-01	United States	USA	PAYEMS	All Employees, Total Nonfarm	30094	—	171	—	—	—	—
1939-05-01	United States	USA	PAYEMS	All Employees, Total Nonfarm	30299	—	199	—	—	—	—

Télécharger un échantillon

Téléchargez l'échantillon complet de ce jeu de données (lignes d'exemple, pas le jeu complet).

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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/labor_signals/us_labor_market_signals" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/labor_signals/us_labor_market_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/labor_signals/us_labor_market_signals

Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.