# IBM releases Granite Timeseries Ensemble, combining four open forecasting models

> **Key takeaway:** IBM's Granite Timeseries Ensemble provides a single interface that combines four complementary open-source time-series forecasting models without requiring retraining or fitted weights.

**Published:** 2026-10-22T07:51:31+00:00 
**Source:** Hugging Face Blog & Announcements 
**Category:** ai-open-models 
**Canonical URL:** https://fosswire.org/news/ibm-releases-granite-timeseries-ensemble-combining-four-open.html 

## Summary
IBM Research has released Granite Timeseries Ensemble, a permissively licensed recipe that combines four pre-trained Granite architectures—Patched Transformer, Patched Conformer, state-space, and MLP mixer models—into a unified forecasting interface. The ensemble returns median and quantile forecasts with no retraining required. Users can choose between a Probabilistic Ensemble with equal fixed weights or an adaptive IQR Ensemble that weights models by forecast spread. The recipe is available through the granite-tsfm Python package and downloads member checkpoints from Hugging Face at runtime.

## How the ensemble combines forecasts
- **Four architectures**: PatchTST-FM-r1 (Patched Transformer), PatchTST-FM-r2 (Patched Conformer), FlowState-r1.1 (state-space model), and TTM-r3 (Tiny Time Mixers/MLP mixer)
- **Probabilistic Ensemble (default)**: uniform probability pooling with equal fixed weights, no fitting step or hyperparameters
- **IQR Ensemble**: adaptive weighting based on each member's forecast spread, giving narrower intervals greater influence
- **Recipe stores configuration, not merged weights**: member checkpoints download at runtime from Hugging Face
- **Outputs**: point forecast via `prediction_outputs` and quantile predictions (0.1 to 0.9 by default) via `quantile_outputs`
- **Requires**: `granite-tsfm>=0.3.11`, context and prediction lengths are configurable parameters

## Why it matters
The ensemble approach reduces the operational burden of model selection for time-series forecasting, which has historically required evaluating multiple models per dataset. By providing a permissively licensed default that works out-of-the-box, IBM lowers the barrier for practitioners who need robust forecasts without deep model-tuning expertise.