Sumanth (@Sumanth_077) “Continuous Intelligence dropped a new frontier time-series foundation model! ci-” — TopicDigg

Continuous Intelligence dropped a new frontier time-series foundation model! ci-proto was built specifically for time-series forecasting, with a context window more than 60× longer than other time-series foundation models. Long context matters differently for time series. A forecast can depend on what happened millions of observations. Seasonal demand can repeat across years, equipment can drift gradually over long periods, and these recurring patterns can disappear completely if the model only sees the most recent window. ci-proto is designed to work with much longer histories directly when predicting what comes next. And because ci-proto is a foundation model, it is not limited to one dataset or industry-specific forecasting setup. The same pretrained model can be applied across different time-series datasets, from energy demand and generation to machine sensor readings and other sequential data. No fine tuning required. On fev-bench, a public benchmark designed to evaluate pretrained forecasting models across different datasets and domains, ci-proto just ranked #1#. And it is open weight, so you can download it and test it on your own data. I've shared the link to the GitHub repo in the comments!