GIFT-Eval
AssessPlatforms
A benchmark for evaluating time-series forecasting models across diverse datasets and scenarios.
Why it's here
Placed in Assess: 1 article(s) of evidence from 1 source(s), led by model releases, with 1 in the last 30 days. Confidence 24%. Low accumulated evidence, so it defaults conservatively pending more signal.
Evidence (1)
- 8Hugging Face Blog·9/9/2026model_releaseIBM releases Granite Time Series PatchTST-FM-r2 with commercial-friendly license
IBM has released Granite Time Series PatchTST-FM-r2, a new time-series foundation model for zero-shot forecasting. The model adds probabilistic forecasting, missing-value imputation, and a larger pretraining corpus, and is dual-licensed under Apache 2.0 and OpenMDW 1.0 for permissive commercial use.