A searchable catalog of forecasting and prediction models, built for students and anyone trying to figure out which model fits their project — and how to actually use it.
Picking the right model for a forecasting or prediction task is hard when there's no single place to browse what's available, compare options, and see working examples. This project aims to be that place.
- Search models by name or tag (e.g. "forecast", "economic")
- Browse results as a grid of model cards
- Each model card includes:
- A description of the model
- Tags for searchability
- Guidance on best use cases and the type of analysis it suits
- Copyable code examples in both Python and R
Main UI is built; no models are loaded yet — they're added by hand. See project_rules/plan.md for the full project plan, including future roadmap (comparisons, favorites, AI-assisted search, accounts, and more).
No build step or server required — open index.html directly in a browser.
Open models.js, copy the example object out of the comment block into the MODELS array, fill in the fields, and save. Refresh the page to see it. See the comment at the top of that file for the field reference.
Faster: with models.js open in VS Code, put your cursor inside the MODELS array, type newmodel, and hit Tab/Enter. A snippet (.vscode/data-analysis-dictionary.code-snippets) inserts the template and tabs you through each field in order.
index.html— page structurestyle.css— stylingmodels.js— the data (edit this to add models)app.js— rendering and search logic