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

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

TL;DR

- General-purpose zero-shot forecasting for demand, prices, energy loads, traffic, telemetry, and other time series.

- Approximately 385M parameters, context length up to 8,192, flexible forecast lengths, and probabilistic forecasts through a 99-quantile prediction head.

- The model backbone is constructed from conformer blocks that combine multi-head self-attention with temporal convolution to capture long- and short-range temporal structure.

- Permissively licensed model with top performance in the replicable zero-shot category of the GIFT-Eval benchmark (dual-licensed under Apache-2.0 and OpenMDW-1.0, users may select either license).

- Available open weights, architecture, inference pipeline, and code to reproduce the benchmark.

Strong zero-shot forecasting on GIFT-Eval

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. GIFT-Eval CRPS for leading replicable zero-shot models. PatchTST-FM-r2 achieves a geometric-mean CRPS of 0.467, placing it immediately behind TimesFM-3 in this comparison, and first among the models with permissive licenses.

Figure 2. GIFT-Eval MASE for leading replicable zero-shot models. PatchTST-FM-r2 achieves a geometric-mean MASE of 0.6846. Blue bars denote models released by the IBM time-series foundation-model team.

Competitive even against models allowed to use benchmark training data

Some models on GIFT-Eval are categorized as pretrained rather than strictly zero-shot. These models are allowed to include the training portions of GIFT-Eval evaluation datasets in their pretraining corpora.

Even when these pretrained models are added to the comparison, PatchTST-FM-r2 remains near the top as seen in Figures 3 and 4: 3rd for CRPS and 4th for MASE among the replicable models. It outperforms several pretrained models, including Chronos-2, Timer-S1, and Toto variants, despite some competing models being considerably larger.

Figure 3. GIFT-Eval CRPS when both zero-shot and pretrained replicable models are considered.

Figure 4. GIFT-Eval MASE when both zero-shot and pretrained replicable models are considered.

Architecture: What changed from PatchTST-FM-r1?