The wide deployment of multi-modal sensors in various areas generates vast amounts of
data with characteristics of high volume, wide variety, and high integrity. However, traditional …

Deep learning based forecasting methods have become the methods of choice in many
applications of time series prediction or forecasting often outperforming other approaches …

Extending the forecasting time is a critical demand for real applications, such as extreme
weather early warning and long-term energy consumption planning. This paper studies the …

Abstract This paper presents Diffusion Forcing, a new training paradigm where a diffusion
model is trained to denoise a set of tokens with independent per-token noise levels. We …

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 …

Time series has wide applications in the real world and is known to be difficult to forecast.
Since its statistical properties change over time, its distribution also changes temporally …

Diffusion models have achieved state-of-the-art performance in generative modeling tasks
across various domains. Prior works on time series diffusion models have primarily focused …

While diffusion models can successfully generate data and make predictions, they are
predominantly designed for static images. We propose an approach for training diffusion …

Forecasting complex time series is ubiquitous and vital in a range of applications but
challenging. Recent advances endeavor to achieve progress by incorporating various deep …

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 …