Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model?

2026-06-07 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

Large Time Series Models (LTSMs) face significant challenges during fine-tuning due to their poorly conditioned, non-convex loss landscapes, which often result in overfitting and performance worse than training from scratch. To address this, researchers propose Smoothed Full Fine-tuning (SFF), a novel technique that constructs an auxiliary LTSM through random initialization. SFF then linearly interpolates the auxiliary model's weights with those of the pre-trained model, effectively smoothing the original loss landscape. This process enhances trainability and preserves pre-trained knowledge, leading to more effective downstream fine-tuning. From an optimization perspective, SFF perturbs sharp minima without affecting flat regions, helping models escape poor local basins towards more generalizable solutions. Extensive experiments demonstrate SFF's consistent improvements across eight representative LTSMs, including Timer, TimesFM, MOMENT, UniTS, MOIRAI, Chronos, TTMs, and Sundial, on various benchmark datasets and tasks. The code is publicly available.

Key takeaway

For Machine Learning Engineers fine-tuning large time series models, directly applying pre-trained LTSMs often leads to suboptimal performance and overfitting due to challenging loss landscapes. You should consider implementing Smoothed Full Fine-tuning (SFF) to overcome these limitations. SFF's approach of smoothing the loss landscape improves trainability and preserves pre-trained knowledge, enabling more effective downstream fine-tuning and better generalization. This method can significantly enhance your model's performance compared to traditional fine-tuning or training from scratch.

Key insights

Smoothed Full Fine-tuning (SFF) improves large time series model trainability by smoothing their non-convex loss landscapes.

Principles

• Non-convex loss landscapes hinder LTSM fine-tuning.
• Smoothing the loss landscape improves generalization.
• Preserving pre-trained knowledge is crucial.

Method

SFF constructs an auxiliary LTSM via random initialization, then linearly interpolates its weights with the pre-trained model's weights to smooth the loss landscape.

In practice

• Apply SFF to fine-tune pre-trained LTSMs.
• Use SFF to mitigate overfitting in LTSMs.
• Explore SFF for models like Timer, TimesFM, MOMENT.

Topics

• Large Time Series Models
• Fine-tuning
• Loss Landscape Optimization
• Model Generalization
• Smoothed Full Fine-tuning
• Deep Learning

Code references

• Meteor-Stars/SFF

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.