US consumer pulse signals (sentiment z-scores, retail/vehicle momentum, anomalies, forecasts)

Name: US consumer pulse signals (sentiment z-scores, retail/vehicle momentum, anomalies, forecasts)
Creator: Consumer Pulse Signals (derived)
License: https://fred.stlouisfed.org/legal
Keywords: consumer-spending, consumer-sentiment, retail-sales, vehicle-sales, volatility, momentum, anomaly-detection, forecasting, signals, fred

Signals derived from FRED's US consumer series: 30-period annualized volatility of monthly changes, 3-month momentum, year-over-year percent change, 3-sigma anomaly flags, naive-drift 1-month forecasts, a per-month cross-series volatility rank, and the University of Michigan sentiment z-score versus its trailing 12-month window. Covers UMCSENT (consumer sentiment index), TOTALSA (total vehicle sales, SAAR) and RSXFS (advance retail sales). 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: Consumer Pulse Signals (derived)1,699 lignesMis à jour: 22/09/2026

consumer-spendingconsumer-sentimentretail-salesvehicle-salesvolatilitymomentumanomaly-detectionforecastingsignalsfred

Qualité

97.5

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. UMCSENT, TOTALSA, RSXFS; 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 (UMCSENT consumer sentiment index, base 1966:Q1=100; TOTALSA vehicle sales in millions of units; RSXFS advance retail sales in millions of dollars); see the series notes for methodology and revisions.
volatility_30d	float
momentum_3m	float
yoy_change_pct	float
anomaly_flag	integer
forecast_1m	float
rank	integer
sentiment_z_12m	float

Exemple de lignes

date	country	country_code	series_id	series_label	value	volatility_30d	momentum_3m	yoy_change_pct	forecast_1m	rank	sentiment_z_12m
1952-11-01	United States	USA	UMCSENT	University of Michigan: Consumer Sentiment	86.2	—	—	—	—	—	—
1953-02-01	United States	USA	UMCSENT	University of Michigan: Consumer Sentiment	90.7	—	—	—	—	—	—
1953-08-01	United States	USA	UMCSENT	University of Michigan: Consumer Sentiment	80.8	—	—	—	—	—	—
1953-11-01	United States	USA	UMCSENT	University of Michigan: Consumer Sentiment	80.7	—	-5.5	—	—	—	—
1954-02-01	United States	USA	UMCSENT	University of Michigan: Consumer Sentiment	82	—	-8.700000000000003	—	—	—	—

Télécharger un échantillon

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

Télécharger l'exemple CSV Télécharger l'exemple JSON

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/consumer_signals/us_consumer_pulse_signals" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/consumer_signals/us_consumer_pulse_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/consumer_signals/us_consumer_pulse_signals

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