Graph Neural Alchemist：一种用于时间序列到图分类的创新性全模块化架构
Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification
AI总结：
本文提出一种基于有向可见性图与PageRank特征的全模块化GNN架构，用于时间序列分类，有效降低了计算复杂度并增强了长程依赖捕获能力，展现出优异的鲁棒性与泛化性。
摘要：
本文介绍了一种基于可见性图表示的全新图神经网络（GNN）架构，用于时间序列分类。传统时间序列分类方法通常面临计算复杂度高以及难以充分捕获时空动态特性的问题。通过将时间序列表示为可见性图，可以编码时间序列数据固有的空间和时间依赖关系，同时具备计算高效性。我们的架构是完全模块化的，能够灵活地使用不同的模型和表示进行实验。我们采用以入度和PageRank特征编码的有向可见性图来改进时间序列的表示，在确保高效计算的同时，增强模型捕获数据中长程依赖关系的能力。我们展示了所提架构在多种分类任务中以及与传统模型对比时的鲁棒性和泛化能力。我们的工作代表了GNN在时间序列分析应用中的一项重大进展，为未来的研究和实际实现提供了一个强大且灵活的框架。
英文摘要：
This paper introduces a novel Graph Neural Network (GNN) architecture for time series classification, based on visibility graph representations. Traditional time series classification methods often struggle with high computational complexity and inadequate capture of spatio-temporal dynamics. By representing time series as visibility graphs, it is possible to encode both spatial and temporal dependencies inherent to time series data, while being computationally efficient. Our architecture is fully modular, enabling flexible experimentation with different models and representations. We employ directed visibility graphs encoded with in-degree and PageRank features to improve the representation of time series, ensuring efficient computation while enhancing the model's ability to capture long-range dependencies in the data. We show the robustness and generalization capability of the proposed architecture across a diverse set of classification tasks and against a traditional model. Our work represents a significant advancement in the application of GNNs for time series analysis, offering a powerful and flexible framework for future research and practical implementations.
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。
