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 …

As a core branch of financial forecasting, stock forecasting plays a crucial role for financial
analysts, investors, and policymakers in managing risks and optimizing investment …

Recently, there has been a growing interest in leveraging pre-trained large language
models (LLMs) for various time series applications. However, the semantic space of LLMs …

Mobility analysis is a crucial element in the research area of transportation systems.
Forecasting traffic information offers a viable solution to address the conflict between …

Recent advances in large language models (LLMs) have unlocked novel opportunities for
machine learning applications in the financial domain. These models have demonstrated …

Time series data are ubiquitous across various domains, making time series analysis
critically important. Traditional time series models are task-specific, featuring singular …

The advent of Transformers marked a significant breakthrough in sequence modelling,
providing a highly performant architecture capable of leveraging GPU parallelism. However …

With recent advances in building foundation models for texts and video data, there is a surge
of interest in foundation models for time series. A family of models have been developed …

In the geospatial domain, universal representation models are significantly less prevalent
than their extensive use in natural language processing and computer vision. This …

Numerous supervised learning models aimed at classifying 12-lead electrocardiograms into
different groups have shown impressive performance by utilizing deep learning algorithms …