Mechanism diagram · Foundation Model
How it works
TimesFM is a decoder-only Transformer pretrained on a large corpus of public time series.
Input series are patched and embedded; the decoder autoregressively generates the forecast horizon.
Operates on raw numerical inputs rather than tokenised quantiles.
Pros and cons on this universe
Pros
- Zero-shot performance competitive with supervised models on standard benchmarks.
- Decoder-only design produces probabilistic forecasts naturally.
- Google publication, broad availability of weights.
Cons / failure modes
- No empirical HL IC — frontier item gated on EXP-012b.
- Crypto distribution shift from Google's pretraining corpus is unknown.
- Compute cost at inference still meaningful relative to mixer arms.