IBM Granite adds a four-model time-series ensemble
- Granite Timeseries Ensemble combines four pre-trained Granite architectures and returns median and quantile forecasts without retraining or fitting ensemble weights.
- Its default Probabilistic Ensemble pools member forecasts with equal influence; an alternative IQR recipe adjusts weights using forecast spread, varying them by sample, target, and horizon step.
- On 97 GIFT-Eval dataset configurations, both ensemble recipes beat their four individual members in aggregate MASE and average MASE rank. The Probabilistic Ensemble recorded 0.683 MASE, compared with 0.685 for PatchTST-FM-r2.
- The configuration downloads four member checkpoints when run, adding inference time and memory compared with one model. The current recipe also forecasts channels independently, without using cross-channel information. These limitations make application-specific backtesting important.
Our take
The small MASE difference from PatchTST-FM-r2 makes this a robustness-versus-resource tradeoff, not an obvious replacement for a strong single model. Backtest both approaches on representative horizons and include the extra checkpoint loading, inference time, and memory in deployment estimates.
Sources
- Ensemble forecasting with IBM Granite time-series models · Hugging Face Blog
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