Granite-TimeSeries-TTM-R2

Granite-TimeSeries-TTM-R2 is IBM's larger-data continuation of the TinyTimeMixer line and the natural step up from R1. IBM positions it as a better-performing follow-on to R1 while preserving the small, fast deployment profile that makes TinyTimeMixer practical on CPUs and lightweight upstream inference. Like R1 it remains a focused family of context- and horizon-specific checkpoints rather than a single universal TSFM, so accuracy comes from matching a checkpoint to your series rather than from raw scale.

At 805K parameters R2 keeps the same compact TinyTimeMixer footprint as R1; the gain comes from training data rather than capacity. Its public forecasting corpus is built from the same domains as R1 — Australian Electricity and Weather, Bitcoin, KDD Cup 2018, London Smart Meters, Saugeen River Flow, Solar, US Births, and wind datasets — but IBM states R2 used about 700M training samples, roughly tripling R1's volume, which is what underpins the accuracy improvement at an unchanged serving cost.