ForecastBench

A dynamic, continuously-updated benchmark to evaluate LLM forecasting capabilities. More at www.forecastbench.org.

Datasets

Leaderboards and datasets are updated nightly and available at github.com/forecastingresearch/forecastbench-datasets.

Participate in the benchmark

Instructions for how to submit your model to the benchmark can be found here: How-to-submit-to-ForecastBench.

Wiki

Dig into the details of ForecastBench on the wiki.

Citation

@inproceedings{karger2025forecastbench,
      title={ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities},
      author={Ezra Karger and Houtan Bastani and Chen Yueh-Han and Zachary Jacobs and Danny Halawi and Fred Zhang and Philip E. Tetlock},
      year={2025},
      booktitle={International Conference on Learning Representations (ICLR)},
      url={https://iclr.cc/virtual/2025/poster/28507}
}

Getting started for devs

Local setup

1. git clone --recurse-submodules <repo-url>.git
2. cd forecastbench
3. cp variables.example.mk variables.mk and set the values accordingly
4. Setup your Python virtual environment
   1. make setup-python-env
   2. source .venv/bin/activate

Run GCP Cloud Functions locally

1. cd directory/containing/cloud/function
2. eval $(cat path/to/variables.mk | xargs) python main.py

Contributions

Before creating a pull request:

• run make lint and fix any errors and warnings
• ensure code has been deployed to Google Cloud Platform and tested (only for our devs, for others, we're happy you're contributing and we'll test this on our end).
• fork the repo
• reference the issue number (if one exists) in the commit message
• push to the fork on a branch other than main
• create a pull request