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A Comprehensive Benchmark of Source-Free Universal Domain Adaptation on Time Series Representations

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arXiv Machine Learning

Oct 2

A Comprehensive Benchmark of Source-Free Universal Domain Adaptation on Time Series Representations

arXiv:2609.39810v1 Announce Type: new Abstract: Source-Free Universal Domain Adaptation (SF-UniDA) extends Universal Domain Adaptation by removing access to source data at adaptation time while still...

By Romain Mussard, Fannia Pacheco, Maxime Berar, Paul Honeine, Gilles Gasso

fine-tuning benchmarks

arXiv AI

Jul 1

Mantis: Lightweight Foundation Model for Time Series Classification

arXiv:2502. 15637v2 Announce Type: replace-cross Abstract: While foundation models have revolutionized various domains, their application to time series classification remains rather under-explored, with existing literature predominantly focused on forecasting.

By Vasilii Feofanov, Songkang Wen, Shifeng Xie, Simon Roschmann, Marius Alonso, Hongbo Guo, Romain Ilbert, Malik Tiomoko, Quentin Bouniot, Zeynep Akata, Lujia Pan, Jianfeng Zhang, Ievgen Redko

llms rag nlp benchmarks

arXiv Machine Learning

Sep 10

PatchFormer: A Patch-Based Time Series Foundation Model with Hierarchical Masked Reconstruction and Cross-Domain Transfer Learning for Zero-Shot Multi-Horizon Forecasting

arXiv:2601.20845v2 Announce Type: replace Abstract: Time series forecasting is a fundamental problem with applications in climate, energy, healthcare, and finance. Many existing approaches require do...

By Olaf Yunus Laitinen Imanov, Derya Umut Kulali, Taner Yilmaz

llms fine-tuning efficiency benchmarks

arXiv Machine Learning

Sep 22

Unlocking Pretrained Vision Transformers for Time Series Classification

arXiv:2506.08641v3 Announce Type: replace Abstract: Adapting vision models for time series analysis is compelling, yet all existing approaches are falling short of dedicated time series foundation mo...

By Simon Roschmann, Quentin Bouniot, Vasilii Feofanov, Ievgen Redko, Zeynep Akata

llms computer-vision fine-tuning benchmarks safety

arXiv Machine Learning

Jul 21

LFM: Leveraging Foundation Models for Source-Free Universal Domain Adaptation

arXiv:2607. 17653v1 Announce Type: cross Abstract: Source-free universal domain adaptation (SF-UniDA) adapts a pre-trained source model to an unlabeled target domain under both covariate and label shifts, without access to source data.

By Jing Li, Pan Liu, Meng Zhao, Wanli Xue, Yanhong Yang, Xu Cheng, Fan Shi, Jianhua Zhang, Qinghua Hu, Shengyong Chen

llms fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 16

Towards a Unified Generative Model for Scarce Time Series with Domain Experts

arXiv:2606. 15172v1 Announce Type: new Abstract: Synthesizing realistic time series with generative models has wide-ranging applications in real-world scenarios.

By Zihao Yao, Qi Zheng, Jiankai Zuo, Yaying Zhang

llms diffusion fine-tuning

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A Comprehensive Benchmark of Source-Free Universal Domain Adaptation on Time Series Representations

Mantis: Lightweight Foundation Model for Time Series Classification

PatchFormer: A Patch-Based Time Series Foundation Model with Hierarchical Masked Reconstruction and Cross-Domain Transfer Learning for Zero-Shot Multi-Horizon Forecasting

Unlocking Pretrained Vision Transformers for Time Series Classification

LFM: Leveraging Foundation Models for Source-Free Universal Domain Adaptation

Towards a Unified Generative Model for Scarce Time Series with Domain Experts