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 …

Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …

Multivariate time series forecasting (MTSF) is crucial for decision-making to precisely
forecast the future values/trends, based on the complex relationships identified from …

Air quality spatiotemporal prediction can provide technical support for environmental
governance and sustainable city development. As a classic multi-source spatiotemporal …

Spatiotemporal time series forecasting plays a key role in a wide range of real-world
applications. While significant progress has been made in this area, fully capturing and …

Can the rapid advances in code generation, function calling, and data analysis using large
language models (LLMs) help automate the search and verification of hypotheses purely …

Spatiotemporal forecasting techniques are significant for various domains such as
transportation, energy, and weather. Accurate prediction of spatiotemporal series remains …

Spatio-temporal prediction tasks play a crucial role in facilitating informed decision-making
through anticipatory insights. By accurately predicting future outcomes, the ability to …

Crime prediction is a crucial yet challenging task within urban computing, which benefits
public safety and resource optimization. Over the years, various models have been …

Transformer-based models have significantly advanced long-term time series forecasting by
leveraging self-attention mechanisms to capture long-term dependencies. However, these …