ConvTimeNet：用于多元时间序列分析的深度层次全卷积模型
ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis
AI总结：
ConvTimeNet提出层次化纯卷积模型，通过可变形补丁层感知局部模式并用大核卷积捕获多尺度依赖，在多元时间序列分析中优于多种模型。
摘要：
设计用于学习时间序列表示的有效模型是时间序列分析的基础。许多先前的工作已经探索了时间序列表示建模方法，并在该领域取得了进展。尽管这些方法有效，但它们缺乏对时间上依赖的基本单元中局部模式的自适应感知，并且未能捕获这些单元之间的多尺度依赖关系。我们不依赖于以自注意力机制为中心的流行方法，而是提出了ConvTimeNet，一种用于时间序列分析的层次化纯卷积模型。ConvTimeNet引入了一个可变形补丁层，以数据驱动的方式自适应地感知时间上依赖的基本单元的局部模式。基于提取的局部模式，设计了层次化纯卷积块，以捕获不同尺度下基本单元表示之间的依赖关系。此外，采用大核机制以确保卷积块可以深度堆叠，从而实现更大的感受野。通过这种方式，局部模式及其多尺度依赖可以在单个模型中有效建模。与各种不同类型模型进行的大量实验表明，纯卷积模型仍然展现出强大的生命力，有效解决了上述两个挑战，并在多个任务中表现出优越的性能。代码可复现。
英文摘要：
Designing effective models for learning time series representations is foundational for time series analysis. Many previous works have explored time series representation modeling approaches and have made progress in this area. Despite their effectiveness, they lack adaptive perception of local patterns in temporally dependent basic units and fail to capture the multi-scale dependency among these units. Instead of relying on prevalent methods centered around self-attention mechanisms, we propose ConvTimeNet, a hierarchical pure convolutional model designed for time series analysis. ConvTimeNet introduces a deformable patch layer that adaptively perceives local patterns of temporally dependent basic units in a data-driven manner. Based on the extracted local patterns, hierarchical pure convolutional blocks are designed to capture dependency relationships among the representations of basic units at different scales. Moreover, a large kernel mechanism is employed to ensure that convolutional blocks can be deeply stacked, thereby achieving a larger receptive field. In this way, local patterns and their multi-scale dependencies can be effectively modeled within a single model. Extensive experiments comparing a wide range of different types of models demonstrate that pure convolutional models still exhibit strong viability, effectively addressing the aforementioned two challenges and showing superior performance across multiple tasks. The code is available for reproducibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
