| --- |
| license: other |
| task_categories: |
| - time-series-forecasting |
| tags: |
| - forecasting |
| - benchmark |
| - time-series |
| - llm-evaluation |
| pretty_name: Frontier Forecasting Arena (FFA) v0.1 |
| size_categories: |
| - n<1K |
| --- |
| |
| # Frontier Forecasting Arena (FFA) — v0.1 (items only) |
|
|
| Anonymized, **answer-free** evaluation items for the Frontier Forecasting Arena, a |
| historical-replay benchmark that measures forecasting skill with proper scoring |
| rules and is designed so that **memorization is measurable rather than assumed |
| away**. |
|
|
| This dataset contains the **model-facing items only** — no ground truth. Skill is |
| verified by the maintainer against held-out truth. That split is the whole point: |
| no real date, absolute level, station, or year ships to a model, so answers can't |
| be looked up or memorized. |
|
|
| ## Contents |
|
|
| | File | Arm | Items | Unit | |
| |---|---|---|---| |
| | `energy/items.jsonl` | Energy load | 50 | 168h history + 24h future drivers → 24×(q10,q50,q90) | |
| | `synthetic/items.jsonl` | Synthetic (DGP) | 50 | contamination-proof floor | |
| | `kalshi/items.jsonl` | Kalshi weather | 50 | settled-market snapshot ladder | |
| | `crypto/items.jsonl` | Crypto | 50 | hourly panel → forward window | |
| | `stock/items.jsonl` | Stock | 50 | daily panel → forward window | |
|
|
| Each line is one JSON item with a compact `columns` + packed-row encoding. |
|
|
| ## How to run it |
|
|
| Use the FFA CLI (code: https://github.com/steves-brain/ffa-benchmark): |
|
|
| ```bash |
| huggingface-cli download userr99/ffa-v0.1 --repo-type dataset --local-dir ffa-v0.1 |
| ffa predict --dataset ffa-v0.1 --out submissions/<your-name> \ |
| --provider openai-compatible --base-url http://localhost:11434/v1 \ |
| --model <your-model> --harness Ollama --dataset-version v0.1 |
| ``` |
|
|
| `ffa predict` never sees the answers. Send the resulting submission back (see |
| `CONTRIBUTING-SUBMISSIONS.md` in the GitHub repo) to be scored against private |
| truth and added to the leaderboard. |
|
|
| ## Scoring spine |
|
|
| CRPS for the continuous arms, Brier/log-loss for binary. Never a single blended |
| skill number — skill is a per-arm vector; only calibration aggregates across arms. |
|
|
| ## License |
|
|
| See the repository for terms. Built from free public data (EIA, NOAA/NWS, Kalshi, |
| public crypto/stock markets), anonymized. |
|
|
