Granite-TimeSeries-TTM-R1

Granite-TimeSeries-TTM-R1 is the entry point to IBM's TinyTimeMixer line, and this catalog entry maps the live `ttm-v1` deployment to IBM's official TTM-R1 family surface. TinyTimeMixer is a compact forecasting architecture built for fast zero-shot and few-shot forecasting on standard public benchmarks. Rather than ship a single universal dense model, the family specializes checkpoints for specific context and prediction lengths, which is what keeps each one small and quick to serve. At 805K parameters it is the smallest and most deployment-friendly IBM checkpoint in the hosted catalog.

The TinyTimeMixer architecture is built to run efficiently on CPUs and lightweight inference paths rather than to maximize raw capacity. R1 was pretrained on public Monash forecasting datasets — Australian Electricity and Weather, Bitcoin, KDD Cup 2018, London Smart Meters, Saugeen River Flow, Solar, US Births, and wind datasets — and IBM states R1 used about 250M public training samples, giving it solid zero-shot and multivariate coverage across those domains.

On TSFM.ai reach for R1 when you want the cheapest, highest-throughput IBM forecaster and your series fall within the context and horizon a TTM checkpoint targets. Step up to TTM-R2 for the larger-data continuation that keeps the same tiny footprint while improving accuracy, or to TTM-R3 for the probabilistic, decomposition-aware refresh of the family.