---
type: "post"
id: "ensemble-forecasting-with-ibm-granite-time-series-models-18fae7"
slug: "ensemble-forecasting-with-ibm-granite-time-series-models-18fae7"
title: "Ensemble forecasting with IBM Granite time-series models"
description: "IBM Research published Granite Timeseries Ensemble R1 on October 22, 2026, a preconfigured ensemble for zero-shot time-series forecasting."
retrieval_nugget: "IBM Research published Granite Timeseries Ensemble R1 on October 22, 2026, a preconfigured ensemble for zero-shot time-series forecasting."
published_at: "2026-10-22"
updated_at: "2026-10-22"
record_date: "2026-10-22"
date_kind: "published_at"
topics: ["models"]
entities: ["IBM Research","Granite Timeseries Ensemble R1","Granite PatchTST-FM-r1","Granite PatchTST-FM-r2","Granite FlowState-r1.1","Granite TTM-r3","GIFT-Eval","Hugging Face"]
related_patterns: []
novelty_label: "new_fact"
what_changed: "IBM introduces a four-model Granite time-series ensemble with probabilistic and adaptive IQR pooling; the prior stories covered Mistral and NVIDIA forecasting releases, not this ensemble."
prior_refs: ["memory:374a02f5-289b-8a8b-ac2a-e6d9675af05a","memory:f94ecfcf-34cd-853b-a921-abd2aaa485d5"]
source_url: "https://huggingface.co/blog/ibm-research/granite-timeseries-ensemble-r1"
source_urls: ["https://huggingface.co/blog/ibm-research/granite-timeseries-ensemble-r1"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:ensemble-forecasting-with-ibm-granite-time-series-models-18fae7"
status: "published"
visuals: []
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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---

# Ensemble forecasting with IBM Granite time-series models

## Retrieval answer

IBM Research published Granite Timeseries Ensemble R1 on October 22, 2026, a preconfigured ensemble for zero-shot time-series forecasting.

IBM Research published Granite Timeseries Ensemble R1 on October 22, 2026, a preconfigured ensemble for zero-shot time-series forecasting. It is loaded through `granite-tsfm>=0.3.11` as `ibm-granite/granite-timeseries-ensemble-r1`; the configuration downloads four checkpoints at runtime: Granite PatchTST-FM-r1, PatchTST-FM-r2, FlowState-r1.1, and TTM-r3.

The default Probabilistic Ensemble pools member quantile forecasts with equal influence and requires neither retraining nor fitted weights. An optional IQR Ensemble changes influence by forecast spread. Both return point forecasts and quantiles, with the default output spanning 0.1 to 0.9. The recipe and its default Granite members are described as permissively licensed; expanded recipes can carry separate non-commercial or research-use terms.

On 97 GIFT-Eval configurations, the default ensemble reported MASE of 0.683, versus 0.685 for PatchTST-FM-r2, the strongest individual member on that measure. PatchTST-FM-r2 reported the best CRPS/MWQL, 0.467 versus 0.471 for each ensemble variant. These aggregate results do not establish accuracy, calibration, latency, memory use, or licensing suitability for a particular dataset or deployment.

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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