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.

翻译：数据增强是时间序列预测中的关键工具，尤其对于需要大量训练样本才能有效泛化的深度学习架构而言。然而，在实际应用场景中，大规模数据集往往难以获得。尽管已有多种数据增强方法，但其局限性在于所使用的变换未能充分保持数据的固有特性。本文提出Grasynda，一种新颖的基于图结构的合成时间序列生成方法，该方法：（1）将单变量时间序列通过图表示转化为网络结构，其中每个状态作为节点，每个转移表示为有向边；（2）通过转移概率矩阵编码其时序动态特征。我们针对Grasynda作为时间序列预测的数据增强方法进行了全面评估，在六个基准数据集上使用了三种神经网络变体进行实验。结果表明，Grasynda在性能上持续优于其他时间序列数据增强方法，包括当前最先进的时间序列基础模型所采用的方法。本方法及全部实验代码均已公开。