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

IBM has released Granite Time Series PatchTST-FM-r2, a roughly 385-million-parameter foundation model for zero-shot forecasting, missing-value imputation, and probabilistic predictions across applications such as demand, energy, prices, traffic, and telemetry. The model uses a redesigned conformer-based architecture that combines self-attention for long-range patterns with temporal convolutions for local structure, along with overlapping Hamming-weighted patches, supports contexts of up to 8,192 steps, and produces point forecasts plus 99 quantiles. On the GIFT-Eval benchmark as of September 8, 2026, IBM reports that it ranks second among replicable zero-shot models for both CRPS and MASE and is the highest-performing model in that group with a permissive commercial-friendly license, while also remaining competitive with models trained on benchmark-related data. IBM provides the weights, code, architecture, inference pipeline, and documented pretraining sources under dual Apache 2.0 and OpenMDW 1.0 licenses, with implementation compatible with earlier PatchTST-FM-r1 checkpoints. The model can be loaded from Hugging Face for forecasting without fine-tuning, and IBM also highlights broader Granite Time Series integrations with Confluent Cloud for real-time forecasting and anomaly detection on streaming data.

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