it automates and explains complex forecasting workflows: you can query data in natural language, compare across models, build ensembles, and ask why forecasts look the way they do. The goal is to make time series analysis more accessible while maintaining professional-grade accuracy.

we benchmarked it on Salesforce’s GIFT-Eval (144k series, 177M datapoints), where it ranked 1st overall (CRPS metric), and it’s already attracting early adopters in the open-source forecasting community.

repo: github.com/azulgarza/timecopilot docs: timecopilot.dev

would love feedback from the HN community, especially on use cases you’d like to see, and how we can make advanced forecasting more useful/usable.