forecasting

IBM Research released Granite Timeseries Ensemble R1, a recipe that combines four pre-trained Granite forecasters into one median and quantile forecast with no retraining or fitted weights. On 97 GIFT-Eval configurations, the Probabilistic Ensemble scores 0.683 MASE, against 0.685 for the best single member.

NVIDIA has released Kumo Forecast (formerly NV-Tesseract Forecasting), a multivariate time-series forecasting model under Apache 2.0. It pairs a frozen MOMENT-1-large encoder of roughly 340 million parameters with a trainable forecasting head, optional nearest-neighbor retrieval over historical data (DARR), and a built-in interpretability framework.

Mistral AI has released Timestral-209M, the largest of four time series foundation models (5M, 28M, 123M, 209M parameters). According to the model card, it leads GIFT-Eval, TIME and fev-bench in zero-shot forecasting, with a context of up to 32,768 time steps and 21-quantile probabilistic output.

Mistral has released Timestral, its first family of time-series foundation models, in four sizes from 5M to 209M parameters. Mistral claims the largest model ranks first among pretrained zero-shot models on GIFT-Eval, TIME and fev-bench, and runs on CPUs without a GPU.

IBM has released Granite Time Series PatchTST-FM-r2, a 385M-parameter forecasting model that ranks #2 overall among replicable zero-shot models on the GIFT-Eval benchmark and first among those with permissive, commercial-friendly licensing. The model uses conformer-based architecture and supports probabilistic forecasts across up to 8,192-step contexts.