TSLANet: Rethinking Transformers for Time Series Representation Learning

• URL: http://arxiv.org/abs/2404.08472v2
• Date: Mon, 6 May 2024 04:00:17 GMT
• Title: TSLANet: Rethinking Transformers for Time Series Representation Learning
• Authors: Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Xiaoli Li,
• Abstract summary: Time series data is characterized by its intrinsic long and short-range dependencies. We introduce a novel Time Series Lightweight Network (TSLANet) as a universal convolutional model for diverse time series tasks. Our experiments demonstrate that TSLANet outperforms state-of-the-art models in various tasks spanning classification, forecasting, and anomaly detection.
• Score: 19.795353886621715
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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