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29 lines
2.2 KiB
Markdown
29 lines
2.2 KiB
Markdown
# TSLANet: Rethinking Transformers for Time Series Representation Learning [[Paper](https://arxiv.org/pdf/2404.08472.pdf)] [[Cite](#citation)]
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#### *by: Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu,and Xiaoli Li*
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## Abstract
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<p align="center">
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<img src="misc/TSLANet.png" width="600" class="center">
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</p>
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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 <b>T</b>ime <b>S</b>eries <b>L</b>ightweight <b>A</b>daptive <b>Net</b>work (<b>TSLANet</b>), 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.
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## Citation
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If you found this work useful for you, please consider citing it.
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```
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@article{tslanet,
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title = {TSLANet: Rethinking Transformers for Time Series Representation Learning},
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author = {Eldele, Emadeldeen and Ragab, Mohamed and Chen, Zhenghua and Wu, Min and Li, Xiaoli},
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journal = {arXiv preprint arXiv:2404.08472},
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year = {2024},
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}
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```
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## Acknowledgements
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The codes in this repository are inspired by the following:
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- GFNet https://github.com/raoyongming/GFNet
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- Masking task is from PatchTST https://github.com/yuqinie98/PatchTST
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- Forecasting and AD datasets are downloaded from TimesNet https://github.com/thuml/Time-Series-Library
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