NanoForecast v0.5 Outperforms TimesFM with 31x Fewer Parameters
September 14, 2026
NanoForecast v0.5 uses a 6.5M-parameter architecture to beat TimesFM (200M) and PatchTST on ETT datasets. Performance gains were achieved through training pipeline optimizations, including corrected loss-scope handling and wider augmentation, rather than architecture changes.
HOW THIS AFFECTS YOU
●
builderYou can deploy highly competitive time-series forecasting with significantly lower latency and compute costs.
●
founderThis demonstrates that training pipeline precision can substitute for massive model scaling.