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TS-PET: A Novel Framework for Fine-Tuning Pretrained Time-Series Models.

Liyang Zheng¹, Baisuo Jin¹

¹University of Science and Technology of China, Hefei, China.

|April 28, 2026

Summary

This summary is machine-generated.

We introduce Time Series Parameter-Efficient Transformer (TS-PET), a new framework for adapting time series foundation models. TS-PET enhances efficiency and accuracy by reducing model parameters and optimizing low-rank adaptation (LoRA).

Keywords:

fine-tuning low-rank adaptation parameter-efficient fine-tuning pretrained transformer models time series forecasting

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Area of Science:

Artificial Intelligence
Machine Learning
Time Series Analysis

Background:

Foundation models for time series offer strong zero-shot abilities.
Existing adaptation methods face challenges like inefficient fine-tuning and overfitting due to computational constraints and parameter-heavy prediction heads.

Purpose of the Study:

To propose a novel fine-tuning framework, Time Series Parameter-Efficient Transformer (TS-PET), for efficient adaptation of time series foundation models.
To address the limitations of current adaptation techniques, specifically concerning computational efficiency and overfitting.

Main Methods:

Developed a lightweight prediction module reducing parameters by over 80% to mitigate overfitting.
Implemented specialized pruned low-rank adaptation (LoRA) with robust rank allocation for improved efficiency.
Conducted extensive experiments on eight diverse benchmarks.

Main Results:

TS-PET achieved state-of-the-art accuracy, outperforming existing methods like MOMENT, PatchTST, and adaptive LoRA variants.
Demonstrated superior parameter efficiency compared to current adaptation techniques.
Successfully enabled scalable adaptation without performance compromise.

Conclusions:

TS-PET offers a highly efficient and accurate solution for adapting time series foundation models.
The proposed framework effectively tackles challenges in fine-tuning and parameter efficiency.
TS-PET facilitates broader adoption and application of foundation models in time series analysis.