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Linear models with optimized preprocessing match advanced architectures in time-series forecasting

By PulseAugur Editorial · [3 sources] ·

Researchers propose that optimizing preprocessing, rather than scaling model architectures, can significantly improve time-series forecasting accuracy. Using Ridge regression as a testbed, they found that optimal lookback periods are series-specific and can be non-monotonic with forecast horizon. Normalizing over a learned fraction of context and adjusting cross-series hyperparameter sharing also proved beneficial. These optimized linear models outperformed prior linear methods and even surpassed Transformer, MLP, and CNN baselines on several benchmarks. AI

IMPACT Suggests that simpler, more efficient models can achieve state-of-the-art performance with proper tuning, potentially reducing computational costs.

RANK_REASON Academic paper presenting novel research findings on time-series forecasting methods.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Linear models with optimized preprocessing match advanced architectures in time-series forecasting

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Academic paper presenting novel research findings on time-series forecasting methods.
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93 days old
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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Lang Huang, Jinglue Xu, Luke Darlow ·

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

    arXiv:2606.27282v1 Announce Type: new Abstract: Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation models, on the assumption that capacity is what unlocks accuracy. We take the opposit…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation models, on the assumption that capacity is what unlocks accuracy. We take the opposite position: most of the gap can be closed at far…

  3. arXiv cs.LG TIER_1 English(EN) · Luke Darlow ·

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

    Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation models, on the assumption that capacity is what unlocks accuracy. We take the opposite position: most of the gap can be closed at far…

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