This work summarizes two ways to accomplish Time-Series (TS) tasks in today's Large
Language Model (LLM) context: LLM-for-TS (model-centric) designs and trains a …

Long series time forecasting has become a popular research direction in recent years, due
to the ability to predict weather changes, traffic conditions and so on. This paper provides a …

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …

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 …

Self-supervised learning has garnered significant attention for its ability to learn meaningful
representations. Recent advancements have introduced self-supervised methods for time …

Machine learning techniques are now integral to the advancement of intelligent urban
services, playing a crucial role in elevating the efficiency, sustainability, and livability of …

Objective. Physiological data are often low quality and thereby compromises the
effectiveness of related health monitoring. The primary goal of this study is to develop a …

Traffic flow prediction is the foundation of traffic scheduling and a major component of
intelligent transportation systems (ITSs). Accurate traffic flow prediction is crucial for …

Multivariate time-series data in numerous real-world applications (eg, healthcare and
industry) are informative but challenging due to the lack of labels and high dimensionality …

Major solar flares are abrupt surges in the Sun's magnetic flux, presenting significant risks to
technological infrastructure. In view of this, effectively predicting major flares from solar …