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

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 …

Although pre-trained transformers and reprogrammed text-based LLMs have shown strong
performance on time series tasks, the best-performing architectures vary widely across …

Microservice architecture has become a driving force in enhancing the modularity and
scalability of web applications, as evidenced by the Alipay platform's operational success …

Nonadiabatic dynamics is key for understanding solar energy conversion and
photochemical processes in condensed phases. This often involves the non-Markovian …

Accurate forecasting of photovoltaic (PV) power is essential for grid scheduling and energy
management. In recent years, deep learning technology has made significant progress in …

Time series data typically contain complex dynamic patterns, which not only include linear
trends and seasonal variations but also significant nonlinear changes and complex …

The balance between model capacity and generalization has been a key focus of recent
discussions in long-term time series forecasting. Two representative channel strategies are …

Deep learning for time series forecasting has seen significant advancements over the past
decades. However, despite the success of large-scale pre-training in language and vision …