SFF (Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model? Published in ICLR 2026)

Pytorch implementation of SFF. The paper is available at the link Paper (PDF).

Overview

Datasets

The public datasets can be downloaded from https://drive.google.com/drive/folders/1PPLsAoDbv4WcoXDp-mm4LFxoKwewnKxX and place them in the datasets folder.

Usage

Timer's pre-trained weights can be downloaded from the link https://drive.google.com/drive/folders/15oaiAl4OO5gFqZMJD2lOtX2fxHbpgcU8.

In the run.py script, different evaluation modes are enabled by setting training_from_scratch (TFS), LP (linear probing), LPFF (linear probing first then full fine-tuning) or smoothed_full_finetuning. If both are set to False, the original full fine-tuning (FF) strategy is adopted.

Reference

If this repository and the work are helpful to you, please consider citing it:

@inproceedings{zhanglost,
  title={Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model?},
  author={Zhang, Xu and Wang, Peng and Wang, Wei},
  booktitle={The Fourteenth International Conference on Learning Representations}
}