IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license

Hugging Face tL;DR Strong zero-shot forecasting on GIFT-Eval Competitive even against models allowed to use benchmark training data Architecture: What changed from PatchTST-FM-r1? Training data Open for research experimentation — and for commercial use Try PatchTST-FM-r2 in a few lines of Python From notebooks to streaming time series High-performance zero-shot forecasting with commercial-friendly open licensing Time-series foundation models are changing the way forecasting systems are built.

Instead of training and maintaining a separate model for every dataset, users can use a pretrained model and generate forecasts zero-shot. IBM has released Granite Time Series PatchTST-FM-r2, the latest model in the Granite TSFM family (github, blog). PatchTST-FM-r2, a new version of its predecessor PatchTST-FM-r1, combines an updated architecture, a larger pretraining corpus, probabilistic forecasting, support for imputation of missing values, and strong zero-shot performance in a ~385M-parameter model. As of September 8, 2026, the model is the top performing zero-shot model released under a permissive, commercial-friendly open-source license (Apache 2.0 and OpenMDW 1.0) among replicable, zero-shot models on the GIFT-Eval leaderboard. GIFT-Eval is a comprehensive time series forecasting benchmark designed to evaluate models across diverse forecasting scenarios; the model ranks #2 overall among replicable, zero-shot models. The model weights, architecture, inference pipeline, and code needed to reproduce the benchmark results are all available. In this blog we describe the model, dive deeper into the benchmarking results and the model architecture, discuss the training data and licensing, and provide code examples illustrating how to use the model. Finally, we also highlight how the models from the Granite Time Series family can be used in streaming applications in production setting leveraging Confluent product. Ready to try it? Open Granite Time Series PatchTST-FM-r2 on Hugging Face A foundation model is most useful when it generalizes to time series it has not been specifically trained on. For that reason, we focus first on zero-shot performance. GIFT-Eval provides a broad evaluation of forecasting models across heterogeneous datasets and forecasting scenarios. When restricting the leaderboard to models that are zero-shot, replicable, and evaluated without test leakage, PatchTST-FM-r2 ranks second for both CRPS and MASE as of September 8, 2026, as illustrated in Figures 1 and 2 (lower values are better for both metrics). Importantly, PatchTST-FM-r2 is the highest-performing model in the same category among models with permissive, commercial-friendly licensing. Figure 1.