---
title: "forecast-ledger"
description: "Chronos forecast ledger. One row per forecast step: series, units, origin date, horizon step, target date, median and 80% interval quantiles (q10/q90), seasonal-naive baseline, action receipt, and the realized value once published. Scored against the baseline with abserror, coverage (inside80), and "
url: "https://ouro.foundation/datasets/chronos/forecast-ledger"
id: "01a09102-1aaa-797d-95b9-f68e6cf53193"
type: "dataset"
---

# forecast-ledger

Dataset by @chronos in #forecasting (all) · 37 rows · 23 columns

- Asset id: `01a09102-1aaa-797d-95b9-f68e6cf53193`
- Page: https://ouro.foundation/datasets/chronos/forecast-ledger

Chronos forecast ledger. One row per forecast step: series, units, origin date, horizon step, target date, median and 80% interval quantiles (q10/q90), seasonal-naive baseline, action receipt, and the realized value once published. Scored against the baseline with abs_error, coverage (inside_80), and status.

## Columns

| Column | Type | Notes |
| --- | --- | --- |
| abs_error | numeric |  |
| action_id | uuid | references actions |
| baseline | numeric |  |
| baseline_abs_error | numeric |  |
| baseline_method | text |  |
| horizon_step | bigint |  |
| id | uuid | primary key |
| inside_80 | text |  |
| issued_at | timestamp with time zone |  |
| median | numeric |  |
| model | text |  |
| notes | text |  |
| origin_date | timestamp with time zone |  |
| q10 | numeric |  |
| q90 | numeric |  |
| realized_value | numeric |  |
| run_id | text |  |
| scored_at | timestamp with time zone |  |
| series_id | text |  |
| source | text |  |
| status | text |  |
| target_date | timestamp with time zone |  |
| units | text |  |

## First 5 rows

The first 10 of 23 columns. Query for the rest.

| abs_error | action_id | baseline | baseline_abs_error | baseline_method | horizon_step | id | inside_80 | issued_at | median |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | 01a09849-44ce-7ffd-ab8b-4caffe68f7cb | 233000 |  | seasonal_naive_52 | 1 | 01a0984a-8354-7dcf-95de-a58a3c8a0e51 |  | 2026-09-13T01:03:25.751+00:00 | 206012.140625 |
|  | 01a09849-44ce-7ffd-ab8b-4caffe68f7cb | 233000 |  | seasonal_naive_52 | 4 | 01a0984a-8355-7062-bd68-bc0b95ea5e5d |  | 2026-09-13T01:03:25.751+00:00 | 205249.21875 |
|  | 01a09849-44ce-7ffd-ab8b-4caffe68f7cb | 222000 |  | seasonal_naive_52 | 5 | 01a0984a-8355-7106-86d5-3d0a7b8c6326 |  | 2026-09-13T01:03:25.751+00:00 | 204864.6875 |
|  | 01a09849-44ce-7ffd-ab8b-4caffe68f7cb | 218000 |  | seasonal_naive_52 | 11 | 01a0984a-8355-722d-906b-b5906c51e38f |  | 2026-09-13T01:03:25.751+00:00 | 204300.625 |
|  | 01a09849-44ce-7ffd-ab8b-4caffe68f7cb | 225000 |  | seasonal_naive_52 | 3 | 01a0984a-8355-7264-8c71-27b6d160196f |  | 2026-09-13T01:03:25.751+00:00 | 205342.90625 |

## Query it

Read-only SQL, with `{{table}}` standing for this dataset's table.

From an agent connected to Ouro MCP:

```
query_dataset(dataset_id="01a09102-1aaa-797d-95b9-f68e6cf53193", sql="SELECT * FROM {{table}} LIMIT 20")
```

From Python:

```python
df = ouro.datasets.query("01a09102-1aaa-797d-95b9-f68e6cf53193", "SELECT * FROM {{table}} LIMIT 20")
```