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

2026-06-25 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

Lang Huang, Jinglue Xu, and Luke Darlow demonstrate that significant accuracy gains in time-series forecasting can be achieved by optimizing preprocessing rather than solely scaling model architectures. Using Ridge regression as a testbed, they explored context length, local normalization, regularization, and augmentation across eight standard benchmarks. Their findings reveal that optimal lookback is strongly series-specific, with fitted power-law exponents ranging from +0.46 on ETTm2 to -0.19 on Exchange and Traffic, challenging the notion that longer horizons always require more history. They also found that normalizing over a learned trailing fraction of the context is almost universally preferred, and the optimal degree of cross-series hyperparameter sharing varies. These optimized linear models outperform prior linear forecasters on most dataset-horizon entries and surpass Transformer, MLP, and CNN baselines on six of eight benchmarks.

Key takeaway

For Machine Learning Engineers optimizing time-series forecasting models, you should prioritize rigorous preprocessing tuning over immediately scaling to larger, more complex architectures. Focus on experimenting with series-specific lookback periods and implementing normalization over a learned trailing fraction of the context. This approach can yield superior performance, often exceeding Transformer or MLP baselines, while offering greater interpretability and lower computational cost.

Key insights

Optimizing preprocessing for linear models can achieve competitive time-series forecasting accuracy, often surpassing complex architectures.

Principles

• Optimal lookback is series-specific and non-monotonic.
• Trailing fraction normalization is generally superior.
• Hyperparameter sharing varies across series.

Method

The study used Ridge regression to search context length, local normalization, regularization, and augmentation on eight benchmarks, leveraging its closed-form solution for optimal hyperparameter identification.

In practice

• Experiment with learned trailing fraction normalization.
• Tailor lookback periods per series, not just horizon.
• Evaluate per-series vs. shared hyperparameters.

Topics

• Time-Series Forecasting
• Linear Models
• Ridge Regression
• Hyperparameter Tuning
• Data Preprocessing
• Model Performance Benchmarks

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Data Scientist

Related on AIssential

• FreqLite: A Lightweight Frequency-Decomposed Linear Model with Adaptive Reversible Normalization for Robust Long-Term Time-Series Forecasting — FreqLite is a lightweight, frequency-decomposed linear model with adaptive normalization for robust, efficient long-term time-series forecasting.
• DIY #21 - Step-by-Step Guide to Time Series Forecasting with RNN — Vanilla RNNs effectively forecast cyclical time series data by learning temporal patterns through sequential processing.
• Improving the sharpness in neural network-based parametric post-processing of ensemble forecasts — A penalty term in neural network loss functions can improve forecast sharpness without sacrificing accuracy.
• Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model? — Smoothed Full Fine-tuning (SFF) improves large time series model trainability by smoothing their non-convex loss landscapes.
• Why Powerful Machine Learning Is Deceptively Easy — Methodological rigor in ML is crucial to distinguish genuine model performance from illusory gains.
• Regularisation in Neural Networks — Regularization techniques are crucial for neural networks to prevent overfitting and improve generalization on unseen data.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.