TSLANet: Rethinking Transformers for Time Series Representation Learning
<|reference_start|>TSLANet: Rethinking Transformers for Time Series Representation Learning: Time series data, characterized by its intrinsic long and short-range dependencies, poses a unique challenge across analytical applications. While Transformer-based models excel at capturing long-range dependencies, they face limitations in noise sensitivity, computational efficiency, and overfitting with smaller datasets. In response, we introduce a novel Time Series Lightweight Adaptive Network (TSLANet), as a universal convolutional model for diverse time series tasks. Specifically, we propose an Adaptive Spectral Block, harnessing Fourier analysis to enhance feature representation and to capture both long-term and short-term interactions while mitigating noise via adaptive thresholding. Additionally, we introduce an Interactive Convolution Block and leverage self-supervised learning to refine the capacity of TSLANet for decoding complex temporal patterns and improve its robustness on different datasets. Our comprehensive experiments demonstrate that TSLANet outperforms state-of-the-art models in various tasks spanning classification, forecasting, and anomaly detection, showcasing its resilience and adaptability across a spectrum of noise levels and data sizes. The code is available at https://github.com/emadeldeen24/TSLANet.<|reference_end|>
@article{eldele2024tslanet:,
    title={TSLANet: Rethinking Transformers for Time Series Representation Learning},
    author={Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Xiaoli Li},
    journal={arXiv preprint arXiv:2404.08472},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08472},
    primaryClass={cs.LG stat.ML}
}