Mechanism diagram · MLP / Linear
How it works
RevIN normalises each lookback window: subtract the last value, divide by per-window std.
A single linear layer maps the normalised lookback to the normalised forecast.
RevIN is reversed at output to produce the un-normalised forecast.
Pros and cons on this universe
Pros
- Cheapest possible competitive baseline — one linear layer.
- Useful sanity check — if a deep model cannot beat NLinear-RevIN, the deep model is not contributing.
- Reproducible and fast.
Cons / failure modes
- Negative IC (−0.044) on this universe — below random.
- Confirms the “floor” role but is not deployable as alpha.
- Beaten by every structured architecture above rank 24 on the screen.