Practical Open Weights
For years, time-series forecasting has relied on traditional statistical methods or highly specialized machine learning models. The new paradigm treats time-series data like language, using foundation models that don't need heavy, custom training from scratch.
Granite Time Series is a strong example of this shift. Instead of building a custom ARIMA or Prophet model for every single metric, teams can now use a pre-trained open-weight model to forecast across different domains out-of-the-box, with surprisingly high accuracy.
The Shift
Foundation models have proven they can generalize across languages and code. Now, models like Granite Time Series prove they can generalize across patterns of numbers, offering zero-shot forecasting capabilities that rival custom-built models.
See how the model worksWhy It Matters
Traditional forecasting requires heavy data preparation and model tuning for each specific use case. A foundation model approach allows engineering teams to deploy forecasting APIs much like they deploy LLM chat endpoints, significantly reducing time-to-value.
Read the right-sized AI thesisWhat To Watch
While statistical methods aren't going away, the baseline is shifting. The next interesting phase is seeing how quickly product teams replace their hardcoded forecasting logic with open-weight time-series foundation models.
Explore our models directoryThis changes the economics of enterprise forecasting. When you don't need a dedicated data science team to spin up a new model for every new product line, forecasting becomes a feature you can embed everywhere.
Read the Granite Time Series guide