---
pretty_name: Agentic Forecasting results
tags:
  - time-series
  - forecasting
  - agents
  - benchmark
---

# Agentic Forecasting: results

Live results of general-purpose agents that forecast real-world time series with information
they find themselves, next to time-series foundation models (TSFMs) given human-curated inputs
and the task's official or expert forecast. Every forecast is submitted before its outcome
exists and scored on the first published value. The website reads this dataset directly.

The tasks (ALE Task format) are in
[agentic-forecasting-tasks](https://github.com/rowingchenn/agentic-forecasting-tasks); the
pipeline that runs them is
[agentic-forecasting](https://github.com/rowingchenn/agentic-forecasting).

## Layout

```text
runs/<task>/
├── truth.csv                          first published value per date
├── <run day>/
│   ├── live/<system>.json             live forecasts: agents, TSFMs, expert forecast
│   ├── mock/<system>.json             agents replaying the day offline
│   ├── x/                             X_d: the curated input the TSFMs received
│   └── ale/<mode>-<agent>/<episode>/  ALE episode: trajectory.json, transport and
│                                      execution traces, result.json, lock.json
└── logs/<date>.log                    the pipeline's log of each day
site/                                  the website's JSON
```

A forecast file holds `run_date`, one entry per target date with nine quantiles (or a
`point` for the expert forecast), and its provenance: harness and model, timing, model
calls, tokens and ALE's list-price cost estimate where the model has a listed price.

## Protocol

On each run day, agents receive the instruction, the run date and target dates, and the
open internet; nothing else. TSFMs receive X_d, the target history and covariates the
task's author chose. Each run day is then replayed offline from the web materials the live
agents saved. Up to and including 2026-10-23, live agents also received X_d.

The agents' saved web pages are not included here; they stay with the private Task assets.
Agent trajectories record the commands agents ran and excerpts of the pages they read.

## Sources

Target and covariate data come from their publishers, for example ERCOT public reports and
Open-Meteo (ERA5 reanalysis and ECMWF IFS forecasts, CC BY 4.0). Each X_d carries a
MANIFEST.json with its sources and file hashes.