This paper introduces ConvTimeNet, a novel deep hierarchical fully convolutional network designed to serve as a general-purpose model for time series analysis. The key design of this network is twofold, designed to overcome the limitations of traditional convolutional networks. Firstly, we propose an adaptive segmentation of time series into sub-series level patches, treating these as fundamental modeling units. This setting avoids the sparsity semantics associated with raw point-level time steps. Secondly, we design a fully convolutional block by skillfully integrating deepwise and pointwise convolution operations, following the advanced building block style employed in Transformer encoders. This backbone network allows for the effective capture of both global sequence and cross-variable dependence, as it not only incorporates the advancements of Transformer architecture but also inherits the inherent properties of convolution. Furthermore, multi-scale representations of given time series instances can be learned by controlling the kernel size flexibly. Extensive experiments are conducted on both time series forecasting and classification tasks. The results consistently outperformed strong baselines in most situations in terms of effectiveness.The code is publicly available.

本文介绍了ConvTimeNet，这是一个新型的深层分层全卷积网络，旨在作为时间序列分析的通用模型。该网络的关键设计从两个方面入手，旨在克服传统卷积网络的局限性。第一，我们提出了将时间序列自适应分割为子序列级的补丁，并将其作为基本建模单元，避免了与原始点级时间步长相关的稀疏语义。第二，我们设计了一个完全卷积块，巧妙地集成了深度卷积和点卷积操作，采用Transformer编码器中使用的先进建模风格，使得该主干网络能够有效捕捉全局序列和跨变量依赖，因为它不仅融入了Transformer架构的进展，还继承了卷积的固有属性。此外，通过灵活控制核大小，可以学习给定时间序列实例的多尺度表示。我们在时间序列预测和分类任务上进行了广泛的实验。结果在大多数情况下在有效性方面始终优于强基线。代码已公开发布。

ConvTimeNet: 多变量时序分析的深层分层完全卷积模型