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 …

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 …

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

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 …

While most time series are non-stationary, it is inevitable for models to face the distribution
shift issue in time series forecasting. Existing solutions manipulate statistical measures …

Abstract The utilization of Large Language Models (LLMs) in financial trading has primarily
been concentrated within the stock market, aiding in economic and financial decisions. Yet …

Mainstream person search models aim to jointly optimize person detection and re-
identification (ReID) in a one-step manner. Despite notable progress, existing one-step …

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 …

Spatial-temporal forecasting systems play a crucial role in addressing numerous real-world
challenges. In this paper, we investigate the potential of addressing spatial-temporal …