Mechanism diagram · CNN / TCN
How it works
ModernTCN uses depthwise separable convolutions with very large kernels (typically 31, 51, or 81).
Channel mixing follows depthwise convolution — similar to ConvNeXt design from computer vision.
Squeeze-and-excitation style residual blocks expand the effective receptive field.
Pros and cons on this universe
Pros
- Top of the 918-experiment benchmark on crypto, FX, and equities (RMSE rank #1-#2).
- Modern design — depthwise separable, parallelisable, well-engineered.
- Strong literature support.
Cons / failure modes
- Empirical HL IC essentially zero (+0.0085) — rank #24 of 28.
- Directional accuracy ≈ 50 percent in the 918-paper crypto experiments.
- Best illustration in the program that literature RMSE leaderboards do not transfer to directional rank correlation.