Synthetic Time Series Generation (TSG) is crucial in a range of applications, including data
augmentation, anomaly detection, and privacy preservation. Although significant strides …

Synthetic data generation has emerged as a promising solution to overcome the challenges
which are posed by data scarcity and privacy concerns, as well as, to address the need for …

Graph neural networks (GNNs), especially dynamic GNNs, have become a research hotspot
in spatiotemporal forecasting problems. While many dynamic graph construction methods …

Generating realistic time series data is important for many engineering and scientific
applications. Existing work tackles this problem using generative adversarial networks …

Time-series generation has significant practical importance for decision-making under
uncertainty. Existing generation methods have various limitations such as error …

In recent years, the discovery of brain effective connectivity (EC) networks through
computational analysis of functional magnetic resonance imaging (fMRI) data has gained …

Using machine learning methods to estimate brain effective connectivity networks from
functional magnetic resonance imaging (fMRI) data has gradually become one of the hot …

Dynamic effective connectivity (DEC) is the accumulation of effective connectivity in the time
dimension, which can describe the continuous neural activities in the brain. Recently …

Using machine learning methods to estimate brain effective connectivity networks from
functional magnetic resonance imaging (fMRI) data has garnered significant attention in the …

This paper presents a comprehensive systematic review of generative models (GANs, VAEs,
DMs, and LLMs) used to synthesize various medical data types, including imaging …