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 …

Deep learning, a crucial technique for achieving artificial intelligence (AI), has been
successfully applied in many fields. The gradual application of the latest architectures of …

In the realm of time series forecasting (TSF), it is imperative for models to adeptly discern
and distill hidden patterns within historical time series data to forecast future states …

Transformers have widely adopted attention networks for sequence mixing and MLPs for
channel mixing, playing a pivotal role in achieving breakthroughs across domains. However …

Time series forecasting has attracted significant attention in recent decades. Previous
studies have demonstrated that the Channel-Independent (CI) strategy improves forecasting …

Given the ubiquitous presence of time series data across various domains, precise
forecasting of time series holds significant importance and finds widespread real-world …

Multimodal content, such as mixing text with images, presents significant challenges to
rumor detection in social media. Existing multimodal rumor detection has focused on mixing …

Remote photoplethysmography (rPPG) is a non-contact method for detecting physiological
signals from facial videos, holding great potential in various applications such as healthcare …

Multivariate time series forecasting plays a crucial role in various fields such as finance,
traffic management, energy, and healthcare. Recent studies have highlighted the …

Effective imputation is a crucial preprocessing step for time series analysis. Despite the
development of numerous deep learning algorithms for time series imputation, the …