US trimmed-mean inflation signals (Dallas Fed underlying-inflation gauges)
Monthly underlying-inflation signals from the Dallas Fed trimmed-mean PCE rate (redistributed by FRED, 1977-01 ->): 3-month momentum, 30-month change volatility, 3-sigma anomaly flags vs a trailing 12-month baseline, naive-drift 1-month forecasts, cross-series ranks, the trimmed-mean-vs-headline and trimmed-mean-vs-core PCE spreads, and above-target (>2%) / high-underlying (>3%) regime flags. 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); underlying data: Federal Reserve Bank of Dallas, U.S. Bureau of Economic Analysis.
Qualité
Attribution
Federal Reserve Bank of St. Louis (FRED; underlying data: Federal Reserve Bank of Dallas and U.S. Bureau of Economic Analysis; 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: PCETRIM12M159SFRBDAL (Trimmed Mean PCE Inflation Rate, Federal Reserve Bank of Dallas), PCEPI (Personal Consumption Expenditures: Chain-type Price Index, U.S. Bureau of Economic Analysis), or PCEPILFE (core PCE price index excluding food and energy, BEA). |
| series_label | string | Official FRED series title as published for the series. |
| value | float | 12-month percent change in prices. PCETRIM12M159SFRBDAL is published directly as a 12-month rate; PCEPI and PCEPILFE are converted from index levels to 12-month percent changes in this connector. |
| momentum_3m | float | |
| volatility_30d | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| rank | integer | |
| trimmed_headline_spread | float | |
| trimmed_core_spread | float | |
| above_target_flag | integer | |
| high_underlying_flag | integer |
Exemple de lignes
| date | country | country_code | series_id | series_label | value | momentum_3m | volatility_30d | anomaly_flag | forecast_1m | rank | trimmed_headline_spread | trimmed_core_spread | above_target_flag | high_underlying_flag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1960-01-01 | United States | USA | PCEPI | Personal Consumption Expenditures: Chain-type Price Index | 1.6948034819308955 | — | — | 0 | — | 1 | — | — | 0 | 0 |
| 1960-02-01 | United States | USA | PCEPI | Personal Consumption Expenditures: Chain-type Price Index | 1.6997167138810054 | — | — | 0 | — | 1 | — | — | 0 | 0 |
| 1960-03-01 | United States | USA | PCEPI | Personal Consumption Expenditures: Chain-type Price Index | 1.6920139574692294 | — | — | 0 | — | 1 | — | — | 0 | 0 |
| 1960-04-01 | United States | USA | PCEPI | Personal Consumption Expenditures: Chain-type Price Index | 1.859517708128 | 0.16471422619710463 | — | 0 | — | 1 | — | — | 0 | 0 |
| 1960-05-01 | United States | USA | PCEPI | Personal Consumption Expenditures: Chain-type Price Index | 1.9110790044000847 | 0.2113622905190793 | — | 0 | — | 1 | — | — | 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/trimmed_mean_inflation_signals/us_trimmed_mean_inflation_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/trimmed_mean_inflation_signals/us_trimmed_mean_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/trimmed_mean_inflation_signals/us_trimmed_mean_inflation_signals
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.