Are Synthetic Time-series Data Really not as Good as Real Data?
Fanzhe Fu Junru Chen Jing Zhang Carl Yang Lvbin Ma Yang Yang
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.