TVNet用3D动态卷积提升时序分析性能，兼顾效率与精度。
TVNet: A Novel Time Series Analysis Method Based on Dynamic Convolution and 3D-Variation
- 提出3D视角的时序重排技术，融合片段间、片段内与变量间关系。
- 在5个时序任务中达顶尖表现，计算效率优于主流Transformer和MLP模型。
- 具备更强迁移性与鲁棒性，适合高要求时序建模场景。
随着Transformer与MLP架构的发展，时序分析取得显著进展。然而，卷积神经网络（CNN）在该领域的表现未达预期，限制了其未来应用潜力。本文旨在通过新视角与设计创新，提升CNN在时序分析中的表征能力。具体而言，提出一种考虑片段间、片段内及跨变量维度的新型时序重排技术，并据此构建TVNet——一种基于3D视角的动态卷积网络。该方法保留了CNN的计算高效性，在五个关键时序分析任务中实现当前最优性能，且在效率与精度之间表现出更优平衡，优于主流的Transformer与MLP模型。此外，实验表明TVNet具备更强的迁移能力与鲁棒性，为将CNN应用于高级时序分析任务提供了新思路。
原文摘要 · Abstract (English)
With the recent development and advancement of Transformer and MLP architectures, significant strides have been made in time series analysis. Conversely, the performance of Convolutional Neural Networks (CNNs) in time series analysis has fallen short of expectations, diminishing their potential for future applications. Our research aims to enhance the representational capacity of Convolutional Neural Networks (CNNs) in time series analysis by introducing novel perspectives and design innovations. To be specific, We introduce a novel time series reshaping technique that considers the inter-patch, intra-patch, and cross-variable dimensions. Consequently, we propose TVNet, a dynamic convolutional network leveraging a 3D perspective to employ time series analysis. TVNet retains the computational efficiency of CNNs and achieves state-of-the-art results in five key time series analysis tasks, offering a superior balance of efficiency and performance over the state-of-the-art Transformer-based and MLP-based models. Additionally, our findings suggest that TVNet exhibits enhanced transferability and robustness. Therefore, it provides a new perspective for applying CNN in advanced time series analysis tasks.
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