IBM Introduces Granite Timeseries Ensemble to Combine Four Forecasting Models

IBM introduced Granite Timeseries Ensemble, a permissively licensed recipe that combines four pretrained Granite models in one interface to produce median and quantile forecasts without retraining or fitting ensemble weights. In the Hugging Face blog report’s evaluation across 97 GIFT-Eval configurations, the default ensemble achieved aggregate MASE of 0.683, compared with 0.685 for the strongest individual model. PatchTST-FM-r2 retained the lowest aggregate CRPS/MWQL, so the ensemble did not lead on every reported metric. The recipe defaults to equal-influence probability pooling and also offers an adaptive IQR alternative. It stores an ensemble configuration rather than merged model weights and downloads all four member checkpoints at runtime, adding time to the first run. Running four models also increases inference time and memory use. The recipe and its default Granite members are permissively licensed.

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