用可视图+模块化GNN高效分类时间序列，捕捉长期依赖。
Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification
- 将时间序列转为有向可视图，融合度和PageRank特征
- 在多个任务上优于传统模型，且计算效率高
- 适合需要灵活设计的时序图神经网络研究者
本文提出一种新型图神经网络（GNN）架构用于时间序列分类，基于可视图表示。传统方法常面临计算复杂度高、难以捕捉时空动态的问题。通过将时间序列转化为可视图，可同时编码空间与时间依赖性，且计算高效。所提架构完全模块化，支持灵活组合不同模型与表示方式。采用带入度和PageRank特征的有向可视图，增强对长程依赖的建模能力，同时保证高效计算。实验表明该架构在多样化的分类任务中具备强鲁棒性与泛化能力，显著优于传统模型。本工作推动了GNN在时间序列分析中的应用，提供了一个强大且灵活的研究与实践框架。
原文摘要 · Abstract (English)
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.
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