Traffic prediction is a crucial task in the Intelligent Transportation System (ITS), receiving
significant attention from both industry and academia. Numerous spatio-temporal graph …

Time series forecasting is a critical task that provides key information for decision-making
across various fields. Recently, various fundamental deep learning architectures such as …

The stable periodic patterns present in time series data serve as the foundation for
conducting long-horizon forecasts. In this paper, we pioneer the exploration of explicitly …

Time series forecasting (TSF) plays a pivotal role in many real-world applications. Recently,
the utilization of Large Language Models (LLM) in TSF has demonstrated exceptional …

Transformer-based forecasting methods have been widely applied to forecast long-term
multivariate time series, which achieves significant improvements on extending the …

Multivariate time series forecasting is crucial for various applications, such as financial
investment, energy management, weather forecasting, and traffic optimization. However …

Over the past decades, cloud computing has become a cornerstone of modern
infrastructure. Accurate cloud workload prediction is crucial for assessing quality of service …

This paper introduces SparseTSF, a novel, extremely lightweight model for Long-term Time
Series Forecasting (LTSF), designed to address the challenges of modeling complex …

Transformer-based methods have achieved excellent results in the field of time series
forecasting due to their powerful ability to model sequences and capture their long-term …

Few-shot segmentation was proposed to obtain segmentation results for a image with an
unseen class by referring to a few labeled samples. However, due to the limited number of …