Mechanism diagram · Hybrid / Mixer
How it works
StemGNN learns an inter-series graph adjacency matrix during training — explicitly modelling cross-variate dependencies.
It combines graph convolutional operations (across the learned graph) with spectral temporal blocks (frequency-domain).
Outputs from each spectral-temporal block are aggregated for the final forecast.
Pros and cons on this universe
Pros
- Explicit cross-variate modelling — fits cross-sectional trading thesis.
- Strong BNB long (+0.307 per-coin IC, ties TFT for best).
- Rank #14 IC overall.
Cons / failure modes
- Pairwise IC ρ = +0.999 with BITCN — extreme redundancy.
- No dossier in the research base.
- Learned graph can overfit on small universes.