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

为了应对时间序列数据的长期和短期相关性，我们引入了一种新颖的时间序列轻量级适应网络（TSLANet），它是一种用于不同时间序列任务的通用卷积模型。我们通过引入自适应阈值、傅里叶分析和自我监督学习来增强特征表示、捕捉长期和短期交互并减少噪声。我们的实验证明，TSLANet 在分类、预测和异常检测等各种任务中表现优于现有模型，展示了其在不同噪声水平和数据规模下的稳健性和适应性。