Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

Numerous deep learning architectures have been developed to accommodate the diversity
of time-series datasets across different domains. In this article, we survey common encoder …

The imputation of missing values in time series has many applications in healthcare and
finance. While autoregressive models are natural candidates for time series imputation …

Aiming to build foundation models for time-series forecasting and study their scaling
behavior, we present here our work-in-progress on Lag-Llama, a general-purpose …

Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …

Green artificial intelligence (AI) is more environmentally friendly and inclusive than
conventional AI, as it not only produces accurate results without increasing the …

Time series forecasting has been a widely explored task of great importance in many
applications. However, it is common that real-world time series data are recorded in a short …

Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …

Generative modeling of multivariate time series has remained challenging partly due to the
complex, non-deterministic dynamics across long-distance timesteps. In this paper, we …