EffiCANet: Efficient Time Series Forecasting with Convolutional Attention

• URL: http://arxiv.org/abs/2411.04669v1
• Date: Thu, 07 Nov 2024 12:54:42 GMT
• Title: EffiCANet: Efficient Time Series Forecasting with Convolutional Attention
• Authors: Xinxing Zhou, Jiaqi Ye, Shubao Zhao, Ming Jin, Chengyi Yang, Yanlong Wen, Xiaojie Yuan,
• Abstract summary: EffiCANet is designed to enhance forecasting accuracy while maintaining computational efficiency. EffiCANet achieves the maximum reduction of 10.02% in MAE over state-of-the-art models.
• Score: 12.784289506021265
• License:
• Abstract: The exponential growth of multivariate time series data from sensor networks in domains like industrial monitoring and smart cities requires efficient and accurate forecasting models. Current deep learning methods often fail to adequately capture long-range dependencies and complex inter-variable relationships, especially under real-time processing constraints. These limitations arise as many models are optimized for either short-term forecasting with limited receptive fields or long-term accuracy at the cost of efficiency. Additionally, dynamic and intricate interactions between variables in real-world data further complicate modeling efforts. To address these limitations, we propose EffiCANet, an Efficient Convolutional Attention Network designed to enhance forecasting accuracy while maintaining computational efficiency. EffiCANet integrates three key components: (1) a Temporal Large-kernel Decomposed Convolution (TLDC) module that captures long-term temporal dependencies while reducing computational overhead; (2) an Inter-Variable Group Convolution (IVGC) module that captures complex and evolving relationships among variables; and (3) a Global Temporal-Variable Attention (GTVA) mechanism that prioritizes critical temporal and inter-variable features. Extensive evaluations across nine benchmark datasets show that EffiCANet achieves the maximum reduction of 10.02% in MAE over state-of-the-art models, while cutting computational costs by 26.2% relative to conventional large-kernel convolution methods, thanks to its efficient decomposition strategy.

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