How Good Can Linear Models Be for Time-Series Forecasting?

arXiv·medium signal

This arXiv paper (2026-06-25) argues that most of the accuracy gap between simple models and large time-series foundation models can be closed by tuning preprocessing rather than scaling architecture, using Ridge regression as a closed-form, interpretable testbed where optimal hyperparameters can be read off directly. The result is a pointed counter to the 'capacity unlocks accuracy' assumption driving ever-larger forecasting transformers. For builders, it's a strong reminder to exhaust cheap, interpretable baselines before reaching for foundation models.