State space models (SSMs) have high performance on long sequence modeling but require
sophisticated initialization techniques and specialized implementations for high quality and …

World models are a fundamental component in model-based reinforcement learning
(MBRL). To perform temporally extended and consistent simulations of the future in partially …

Synthetic time series are often used in practical applications to augment the historical time
series dataset, amplify the occurrence of rare events and also create counterfactual …

Effectively modeling long spatiotemporal sequences is challenging due to the need to model
complex spatial correlations and long-range temporal dependencies simultaneously …

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

Limit order books are a fundamental and widespread market mechanism. This paper
investigates the use of conditional generative models for order book simulation. For …

Time series, characterized by a sequence of data points arranged in a discrete-time order,
are ubiquitous in real-world applications. Different from other modalities, time series present …

Substantial recent progress in machine learning has been driven by advances in sequence
models, which form the backbone of deep learning models that have achieved widespread …

Given the remarkable achievements in image generation through diffusion models, the
research community has shown increasing interest in extending these models to video …

Recent proliferation of electric vehicle (EV) charging load has imposed vital stress on power
grid. The stochasticity and volatility of EV charging behaviors render it challenging to …