US inflation signals (CPI/PCE YoY momentum, core gaps, anomalies, forecasts)
Signals derived from FRED's US inflation series: 30-period annualized volatility of monthly changes, 3-month momentum, year-over-year percent change (the headline inflation gauge), 3-sigma anomaly flags, naive-drift 1-month forecasts, a per-month cross-series volatility rank, plus the core-vs-headline CPI gap and the core-PCE-vs-headline spread. Covers CPIAUCSL (headline CPI, seasonally adjusted), CPILFESL (core CPI), PCEPILFE (core PCE price index — the Fed's preferred gauge) and PPIACO (producer prices, all commodities). 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).
Qualité
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. CPIAUCSL, CPILFESL, PCEPILFE, PPIACO; 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 (price indices: CPIAUCSL and CPILFESL with base 1982-1984=100, PCEPILFE with base 2017=100, PPIACO with base 1982=100); 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 | |
| core_headline_gap | float | |
| core_pce_gap | float |
Exemple de lignes
| date | country | country_code | series_id | series_label | value | volatility_30d | momentum_3m | yoy_change_pct | anomaly_flag | forecast_1m | rank | core_headline_gap | core_pce_gap |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1913-01-01 | United States | USA | PPIACO | Producer Price Index by Commodity: All Commodities | 12.1 | — | — | — | 0 | — | — | — | — |
| 1913-02-01 | United States | USA | PPIACO | Producer Price Index by Commodity: All Commodities | 12 | — | — | — | 0 | — | — | — | — |
| 1913-03-01 | United States | USA | PPIACO | Producer Price Index by Commodity: All Commodities | 12 | — | — | — | 0 | — | — | — | — |
| 1913-04-01 | United States | USA | PPIACO | Producer Price Index by Commodity: All Commodities | 12 | — | -0.09999999999999964 | — | 0 | — | — | — | — |
| 1913-05-01 | United States | USA | PPIACO | Producer Price Index by Commodity: All Commodities | 11.9 | — | -0.09999999999999964 | — | 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/inflation_signals/us_inflation_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/inflation_signals/us_inflation_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/inflation_signals/us_inflation_signals
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.