IBM released granite-tsfm 0.3.11, a Python package that bundles four pre-trained time-series models into a single ensemble interface. Calling QuantileEnsembleForecaster.from_pretrained downloads the member checkpoints from Hugging Face and returns both point forecasts and quantile outputs without requiring prior training on your data. The default recipe pools forecasts in probability space using fixed equal weights.
It is worth testing if you need zero-shot probabilistic forecasting across mixed industrial or retail signals and want to skip the overhead of training model weights. The catch is that the first run takes extra time to download all four underlying architectures at runtime, and the package is not built for custom streaming pipelines. You should stick to a single tuned model if you already have clean data and well-defined domains.
more: https://huggingface.co/blog/ibm-research/granite-timeseries-ensemble-r1
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