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

ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for  Multivariate Time Series Analysis

Training deep neural networks often requires careful hyper-parameter tuning
and significant computational resources. In this paper, we propose ConvTimeNet
(CTN): an off-the-shelf deep convolutional neural network (CNN) trained on
diverse univariate time series classification (TSC) source tasks. Once trained,
CTN can be easily adapted to new TSC target tasks via a small amount of
fine-tuning using labeled instances from the target tasks. We note that the
length of convolutional filters is a key aspect when building a pre-trained
model that can generalize to time series of different lengths across datasets.
To achieve this, we incorporate filters of multiple lengths in all
convolutional layers of CTN to capture temporal features at multiple time
scales. We consider all 65 datasets with time series of lengths up to 512
points from the UCR TSC Benchmark for training and testing transferability of
CTN: We train CTN on a randomly chosen subset of 24 datasets using a multi-head
approach with a different softmax layer for each training dataset, and study
generalizability and transferability of the learned filters on the remaining 41
TSC datasets. We observe significant gains in classification accuracy as well
as computational efficiency when using pre-trained CTN as a starting point for
subsequent task-specific fine-tuning compared to existing state-of-the-art TSC
approaches. We also provide qualitative insights into the working of CTN by: i)
analyzing the activations and filters of first convolution layer suggesting the
filters in CTN are generically useful, ii) analyzing the impact of the design
decision to incorporate multiple length decisions, and iii) finding regions of
time series that affect the final classification decision via occlusion
sensitivity analysis.

该研究提出了一种基于深度卷积神经网络的预训练模型 ConvTimeNet（CTN），并通过对多个时间序列分类数据集进行训练和测试，证明了其在新任务上的可迁移性和高效性。