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

Lang Huang, Jinglue Xu, Luke Darlow|June 25, 2026arXiv

Key Takeaway

Before building bigger models, optimize your data preprocessing: context length, normalization strategy, and regularization can close most of the accuracy gap at a fraction of the computational cost.

Summary

This paper shows that simple linear models (Ridge regression) can match or beat complex deep learning architectures for time-series forecasting by carefully tuning preprocessing—context length, normalization, and regularization—rather than scaling model size.

efficiency evaluation data

Key Terms

ridge-regression context-length time-series-forecasting local-normalization hyperparameter-tuning