Abstract
Foundation models for time series analysis (TSA) have attracted significant attention. However, challenges such as training data scarcity and imbalance continue to hinder their development. Inspired by complex dynamic system theories, we design a series-symbol data generation mechanism, enabling the unrestricted creation of high-quality time series data paired with corresponding symbolic expressions. To leverage series-symbol data pairs with strong correlations, we develop \texttt{SymTime}, a pre-trained foundation model for enhancing time series representation using symbolic information. \texttt{SymTime} demonstrates competitive performance across five major TSA tasks when fine-tunes with downstream tasks, rivaling foundation models pre-trained on real-world datasets. This approach underscores the potential of series-symbol data generation and pretraining mechanisms in overcoming data scarcity and enhancing task performance. The code is available at this https URL.
Abstract (translated)
时间序列分析（TSA）领域的基础模型受到了广泛关注，但训练数据稀缺和不平衡等问题仍然阻碍了其发展。受复杂动态系统理论的启发，我们设计了一种系列-符号数据生成机制，该机制能够无限制地创建高质量的时间序列数据及其对应的符号表达式。为了利用具有强相关性的系列-符号数据对，我们开发了一个名为\texttt{SymTime}的基础预训练模型，用于通过符号信息增强时间序列表示。在针对五个主要TSA任务进行微调时，\texttt{SymTime}展示出了与基于真实世界数据集预训练的基础模型相当的性能表现。这种方法强调了系列-符号数据生成和预训练机制在克服数据稀缺性并提升任务性能方面的潜力。代码可在[此处](https://this-url.com)获取。
URL
https://arxiv.org/abs/2510.08445
