Researchers have been actively engaged in recent studies utilizing the denoising diffusion probabilistic model (DDPM), a generative model that shows stable convergence during training, unlike other generative models, and represents data distributions that approximate real-world data. Due to these advantages, DDPM is widely employed for image, video, and audio generation. Recent DDPM research extends beyond image, video, and audio to various datasets and applications. The majority of real-world scenarios involve spatiotemporal data. Consequently, researchers propose prediction models using spatiotemporal data across domains such as traffic forecasting, weather prediction, and disease tracking. While most spatiotemporal data prediction models focus on traffic data, weather and epidemic data present higher uncertainty due to regional variations, seasonal changes, fluctuations in infection rates, and uncertainties in reported cases. To address these challenges of spatiotemporal data, we propose a DDPM-based model. In contrast to previous DDPM studies, we propose a denoising network combining Graph Structure Learning (GSL) to utilize the dynamic nature of spatiotemporal data. We assess performance using three real-world datasets encompassing traffic, weather, and epidemics and conduct an ablation study comparing GSL methods.

Machine learning (ML) models are used to mine inconspicuous information in big data. The model and data quality influence the performance of a ML model. However, modifying the ML model while measuring performance is impractical, and low-quality data causes biased model training. Therefore, improving the data quality is essential. Visual analytics systems supporting DQI (Data Quality Improvement) have been proposed in the past. However, in the studies, it is difficult for users to assess comprehensive data quality improvement methods for machine learning and to determine an appropriate data quality improvement process. In this paper, we propose a novel visual analytics system for managing data quality used in machine learning models.