小波概率循环卷积网络用于多变量时间序列分类
Wavelet Probabilistic Recurrent Convolutional Network for Multivariate Time Series Classification
AI总结：
本文提出小波概率循环卷积网络（WPRCN），通过自适应小波概率特征生成器与通道注意力概率时间卷积网络，结合LSTM和C-FCN，在30个多变量时间序列数据集上平均准确率与排名优于基准，尤其擅长处理数据稀缺、非平稳和噪声扰动。
摘要：
本文提出了一种用于多变量时间序列分类（MTSC）的小波概率循环卷积网络（WPRCN），特别适用于处理非平稳环境、数据稀缺和噪声扰动。我们引入了一个多功能的概率小波模块，用于提取和分析概率特征，该模块可以无缝集成到各种神经网络架构中。该概率模块由自适应小波概率特征生成器（AWPG）和基于通道注意力的概率时间卷积网络（APTCN）组成。这种公式将小波概率神经网络的应用扩展到用于MTSC的深度神经网络。AWPG构建了一个集成概率模型，以应对不同的数据稀缺性和非平稳性；它自适应地选择最优模型，并为APTCN生成概率特征。APTCN分析特征的相关性，并与现有的MTSC模型形成综合特征空间以进行分类。在此，我们将所提出的模块实例化为与长短期记忆（LSTM）网络和因果全卷积网络（C-FCN）并行工作，展示了其在时间序列分析中的广泛适用性。WPRCN在30个不同的MTS数据集上进行了评估，在平均准确率和排名上优于所有基准算法，在处理数据稀缺以及受扰动和非平稳性影响的生理数据方面表现出显著优势。
英文摘要：
This paper presents a Wavelet Probabilistic Recurrent Convolutional Network (WPRCN) for Multivariate Time Series Classification (MTSC), especially effective in handling non-stationary environments, data scarcity and noise perturbations. We introduce a versatile wavelet probabilistic module designed to extract and analyse the probabilistic features, which can seamlessly integrate with a variety of neural network architectures. This probabilistic module comprises an Adaptive Wavelet Probabilistic Feature Generator (AWPG) and a Channel Attention-based Probabilistic Temporal Convolutional Network (APTCN). Such formulation extends the application of wavelet probabilistic neural networks to deep neural networks for MTSC. The AWPG constructs an ensemble probabilistic model addressing different data scarcities and non-stationarity; it adaptively selects the optimal ones and generates probabilistic features for APTCN. The APTCN analyses the correlations of the features and forms a comprehensive feature space with existing MTSC models for classification. Here, we instantiate the proposed module to work in parallel with a Long Short-Term Memory (LSTM) network and a Causal Fully Convolutional Network (C-FCN), demonstrating its broad applicability in time series analysis. The WPRCN is evaluated on 30 diverse MTS datasets and outperforms all the benchmark algorithms on average accuracy and rank, exhibiting pronounced strength in handling scarce data and physiological data subject to perturbations and non-stationarities.
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。
