Time-series data presents limitations stemming from data quality issues, bias
and vulnerabilities, and generalization problem. Integrating universal data
synthesis methods holds promise in improving generalization. However, current
methods cannot guarantee that the generator's output covers all unseen real
data. In this paper, we introduce InfoBoost -- a highly versatile cross-domain
data synthesizing framework with time series representation learning
capability. We have developed a method based on synthetic data that enables
model training without the need for real data, surpassing the performance of
models trained with real data. Additionally, we have trained a universal
feature extractor based on our synthetic data that is applicable to all
time-series data. Our approach overcomes interference from multiple sources
rhythmic signal, noise interference, and long-period features that exceed
sampling window capabilities. Through experiments, our non-deep-learning
synthetic data enables models to achieve superior reconstruction performance
and universal explicit representation extraction without the need for real
data.

本文提出了 InfoBoost—— 一种高度灵活的跨领域数据合成框架，具有时间序列表示学习能力。我们开发了一种基于合成数据的方法，能够在无需真实数据的情况下进行模型训练，并超越了使用真实数据进行训练的模型性能。此外，我们训练了一个基于合成数据的通用特征提取器，适用于所有时间序列数据。我们的方法克服了多个来源的干扰，如周期性信号、噪音干扰和超出采样窗口能力的长期特征。通过实验证明，我们的非深度学习合成数据能够使模型实现卓越的重构性能和通用的显性表示提取，而无需真实数据。