Are Synthetic Time-series Data Really not as Good as Real Data?

TL;DR

This paper introduces InfoBoost, a versatile framework for synthetic time-series data generation that enables training models without real data, surpassing real data performance and providing universal feature extraction.

Contribution

The paper presents a novel synthetic data generation method and a universal feature extractor for time-series data, improving model training and generalization without relying on real data.

Findings

Synthetic data enables superior model reconstruction performance.

Universal feature extractor applies across diverse time-series data.

Method overcomes noise, rhythmic, and long-period feature challenges.

Abstract

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.