Anchor Before You Learn: Zero-Parameter Climatology Beats Learned Normalization

Trainable per-instance normalization is the usual answer to non-stationary demand, and it spends every origin relearning a periodic level profile that one grouped average over the training split hands back exactly. Put a unit-by-hour-of-week climatology table in that slot, pass the network only the residual, then add the table back. The alternative costs nothing. Fix one causal origin-frozen rolling table before any test number, keep it across four backbones from 27k to 682k parameters, five demand systems in two cities and five horizons, run both sides to convergence, and all 100 cells fall to it against the strongest learned normalizer, by 10.55 to 74.26 percent of MAE, median 36.19, each cell clearing a five-seed screen, surviving Holm correction, ahead on RMSE. Inside it sits the frequency-adaptive normalizer built around periodicity, beaten in 100 of 100 at a median 44.67 percent and the pool's best member in 23. Measured against a threshold fixed before the second city was scored, 39 of its 40 cells reproduce and none fails. Independence is thinner than the count implies, five overlapping panels resting on two lattices and one split design. Grade each head on its own table and almost no credit remains. Under the pinned rolling table the 682k patch transformer returns 1.20 percent over fifteen long-horizon cells, the 27k recurrent head 0.21, two of four below nothing. A cost-free rolling climatology keeps 38 of the 100, the other 62 worth a median 0.56 percent. Work out the climatology floor before any normalization block joins a periodic-data comparison.