macroforecast

What macroforecast does: run forecasting research on FRED-MD / FRED-QD / FRED-SD with your own dataset, preprocessing, and models — and benchmark them head-to-head against established methods. One YAML recipe defines the full study; macroforecast.replicate(...) regenerates every artifact identically from the recipe.

Pick your path

🚀 Getting started

Install, quickstart, your first complete study.

Getting started

📝 User guide

Recipe authoring, custom models, FRED & custom datasets.

User guide

📖 Reference

Encyclopedia of every option + architecture design narrative.

Reference

🔁 Replications

Paper replications, recipe gallery, layer navigator.

Replications

🆘 Help

Troubleshooting, contributing, conventions.

Help

Architecture vs Encyclopedia: same 12-layer system, two angles. Architecture is prose — “why is L2 separated from L3”, “how does L7 read L4 sinks”, “what are the cross-layer references”. Encyclopedia is lookup — one page per axis with every option’s definition, when to use, when NOT, references, related options. Architecture is hand-written; encyclopedia is auto-generated from LayerImplementationSpec + OPTION_DOCS and locked by the ci-docs drift gate.

Architecture overview

12-layer canonical design — see architecture. The full 4-part design lives under plans/design/ in the repo.

L0 -> L1 -> L2 -> L3(pipeline) -> L4(pipeline) -> L5 -> L6 -> L7(pipeline) -> L8
        |      |      |       |
       L1.5   L2.5   L3.5    L4.5 diagnostics

Install

pip install macroforecast

See install for extras and source install.

License

MIT