用图结构生成时间序列，提升预测模型训练效果
Grasynda: Graph-based Synthetic Time Series Generation
- 将时间序列转为节点与边的图结构，保留时序动态
- 在6个基准数据集上优于现有增强方法
- 适合需要数据增强的时间序列研究者
数据增强是时间序列预测中的关键工具，尤其对深度学习模型而言，大样本训练才能有效泛化。但在真实场景中，大规模数据并不总可得。尽管已有多种数据增强方法，但其局限在于使用的变换无法充分保持数据特性。本文提出Grasynda，一种基于图结构的合成时间序列生成方法：(1) 将单变量时间序列转化为图结构，每个状态作为节点，每条转移作为有向边；(2) 通过转移概率矩阵编码其时序动态。我们在六个基准数据集上，使用三种神经网络变体对Grasynda进行了全面评估。结果表明，Grasynda在时间序列预测任务中持续优于其他数据增强方法，包括当前先进时间序列基础模型所采用的方法。该方法及所有实验均公开可用。
原文摘要 · Abstract (English)
Data augmentation is a crucial tool in time series forecasting, especially for deep learning architectures that require a large training sample size to generalize effectively. However, extensive datasets are not always available in real-world scenarios. Although many data augmentation methods exist, their limitations include the use of transformations that do not adequately preserve data properties. This paper introduces Grasynda, a novel graph-based approach for synthetic time series generation that: (1) converts univariate time series into a network structure using a graph representation, where each state is a node and each transition is represented as a directed edge; and (2) encodes their temporal dynamics in a transition probability matrix. We performed an extensive evaluation of Grasynda as a data augmentation method for time series forecasting. We use three neural network variations on six benchmark datasets. The results indicate that Grasynda consistently outperforms other time series data augmentation methods, including ones used in state-of-the-art time series foundation models. The method and all experiments are publicly available.
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