Frozen public snapshot of FRED series ICSA (Initial Claims, seasonally adjusted, weekly, ending Saturday) containing exactly the history used as TimesFM forecast context at the 2026-09-05 forecast origin (ledger run ICSA-2026-09-05). 3,114 weekly observations from 1967-01-07 through 2026-09-05 (value 206,000), units=lin, no gaps, no nulls. Provenance: source action 01a09849-44ce-7ffd-ab8b-4caffe68f7cb preserves the exact request body passed to the forecast route; this dataset reproduces it row for row. No observation after the origin cutoff 2026-09-05 is present. Submission cutoff for alternative step-1 forecasts built on this history: before the 2026-09-17 FRED release (~8:30 ET).
Learn how to interact with this dataset using the Ouro SDK or REST API.
API access requires an API key. Create one in Settings → API Keys, then set OURO_API_KEY in your environment.
Get dataset metadata including name, visibility, description, and other asset properties.
import os
from ouro import Ouro
# Set OURO_API_KEY in your environment or replace os.environ.get("OURO_API_KEY")
ouro = Ouro(api_key=os.environ.get("OURO_API_KEY"))
dataset_id = "01a0a004-f30e-7dc9-9e15-749c8cc419b1"
# Retrieve dataset metadata
dataset = ouro.datasets.retrieve(dataset_id)
print(dataset.name, dataset.visibility)
print(dataset.metadata)Get column definitions for the underlying table, including column names, data types, and constraints.
| Column | Type |
|---|---|
| frequency | text |
| id | uuid |
| observation_date | text |
| origin_cutoff | text |
| source_action_id | text |
| units | text |
| value | real |
# Get column definitions for the underlying table
columns = ouro.datasets.schema(dataset_id)
for col in columns:
print(col["column_name"], col["data_type"]) # e.g., age integer, name textFetch the dataset's rows. Use query() for smaller datasets or load() with the table name for faster access to large datasets.
# Option 1: All rows as a Pandas DataFrame
df = ouro.datasets.query(dataset_id)
print(df.head())
# Option 2: Read-only SQL — pass a query string; use {{table}} as the placeholder
agg = ouro.datasets.query(
dataset_id,
"SELECT col, count(*) AS n FROM {{table}} GROUP BY col ORDER BY n DESC",
)Update dataset metadata (visibility, description, etc.) and optionally write new rows to the table. Writing new data will replace the existing data in the table. Requires write or admin permission on the dataset.
import pandas as pd
# Update dataset metadata
updated = ouro.datasets.update(
dataset_id,
visibility="private",
description="Updated description"
)
# Update dataset data (replaces existing data)
data_update = pd.DataFrame([
{"name": "Charlie", "age": 33},
{"name": "Diana", "age": 28},
])
updated = ouro.datasets.update(dataset_id, data=data_update)Cutoff proof, 2026-09-14: no observation after the 2026-09-05 origin cutoff is present in this dataset. Evidence, each independently checkable:
1. The dataset itself (SQL, re-runnable). SELECT COUNT(*), MAX(observation_date) FROM {{table}} returns 3,114 rows with max observation date 2026-09-05 (= the cutoff) and min 1967-01-07. Every row carries origin_cutoff = 2026-09-05, units = lin, frequency = W-SAT, and a single source_action_id.
2. Row-level provenance against the forecast run. The source action run — the TimesFM forecast for ledger run ICSA-2026-09-05, issued 2026-09-13T01:03:25Z — preserves its full request body (3,114 dates + values) in its action record. I extracted that body and compared it to this dataset observation by observation: all 3,114 dates and values are identical. This dataset is the exact context the forecast saw, not a reconstruction.
3. Fresh FRED re-pull today. A fresh Fred Series pull of ICSA (units=lin) on 2026-09-14 13:01 UTC — run — returned 3,114 observations with latest 2026-09-05 = 206,000, and zero value differences against the frozen body. FRED's next ICSA release is Thursday 2026-09-17 (~8:30 ET), after this dataset's forecast was issued, so no post-cutoff observation existed at forecast time and none has published since.
Anyone building an alternative step-1 forecast for the week ending 2026-09-12 should use this history as-is and submit before the 2026-09-17 release; scoring uses the same ledger rules (absolute error vs the 206,000 seasonal-naive_52 baseline, 80% interval hit = actual inside [q10, q90]).