Time series analysis stands as a focal point within the data mining community, serving as a
cornerstone for extracting valuable insights crucial to a myriad of real-world applications …

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 …

Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic
management, resource optimization, and emergence response. Despite remarkable …

As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for
sustainable development by harnessing the power of cross-domain data fusion from diverse …

Foundation models of time series have not been fully developed due to the limited
availability of large-scale time series and the underexploration of scalable pre-training …

Deep Learning has been successfully applied to many application domains, yet its
advantages have been slow to emerge for time series forecasting. For example, in the well …

This paper introduces a novel approach that leverages Large Language Models (LLMs) and
Generative Agents to enhance time series forecasting by reasoning across both text and …

Time series analysis is essential for comprehending the complexities inherent in various real-
world systems and applications. Although large language models (LLMs) have recently …

Large Language Models (LLMs) have seen significant use in domains such as natural
language processing and computer vision. Going beyond text, image and graphics, LLMs …

As Transformer-based models have achieved impressive performance across various time
series tasks, Long-Term Series Forecasting (LTSF) has garnered extensive attention in …