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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; the pipeline that runs them is agentic-forecasting.
Layout
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.
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