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

Integrating universal Issues: The fine-tuning process for temporal data needs data synthesis methods holds promise in improving to be handled carefully as it may contain adversarial or noisy generalization. However, current methods cannot examples, which could impact the model's robustness; (2) guarantee that the generator's output covers Bias and Vulnerabilities: The use of temporal data may all unseen real data. In this paper, we introduce cause the model to inherit biases or vulnerabilities from the InfoBoost-a highly versatile cross-domain data data, thereby reducing its robustness in real-world applications; synthesizing framework with time series representation (3) Generalization Problems: Despite being trained learning capability. We have developed on vast datasets, time-series models may not generalize a method based on synthetic data that enables well to unseen or out-of-distribution data. Time-series and model training without the need for real data, surpassing spatio-temporal data may exhibit sudden shifts or trends, the performance of models trained with potentially leading to unreliable outputs, highlighting the real data. Additionally, we have trained a universal need for robust generalization (Jin et al., 2023).