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.

翻译：时间序列数据以其固有的长短程依赖性为特征，给各类分析应用带来了独特挑战。尽管基于Transformer的模型擅长捕捉长程依赖关系，但在噪声敏感性、计算效率以及小数据集上的过拟合方面存在局限性。为此，我们提出了一种新型时间序列轻量级自适应网络（TSLANet），作为一种适用于多种时间序列任务的通用卷积模型。具体而言，我们提出了自适应频谱块，利用傅里叶分析增强特征表示，并通过自适应阈值化捕获长期和短期交互作用同时抑制噪声。此外，我们引入了交互卷积块，并利用自监督学习来优化TSLANet解码复杂时间模式的能力，提高其在不同数据集上的鲁棒性。我们的综合实验表明，TSLANet在分类、预测和异常检测等多种任务中均优于最先进的模型，展示了其在各种噪声水平和数据规模下的鲁棒性和适应性。代码见https://github.com/emadeldeen24/TSLANet。