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 …

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

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

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

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 …

Lately, there has been a surge in interest surrounding generative modeling of time series
data. Most existing approaches are designed either to process short sequences or to handle …

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

From finance and healthcare to transportation and beyond, effective time series modeling
underpins a wide range of applications. While transformers have achieved success, their …

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