Time series forecasting has played the key role in different industrial, including finance,
traffic, energy, and healthcare domains. While existing literatures have designed many …

Multivariate time series (MTS) forecasting has shown great importance in numerous
industries. Current state-of-the-art graph neural network (GNN)-based forecasting methods …

Deep learning models have progressively advanced time series forecasting due to their
powerful capacity in capturing sequence dependence. Nevertheless, it is still challenging to …

Long-term time series forecasting has gained significant attention in recent years. While
there are various specialized designs for capturing temporal dependency, previous studies …

Deep learning, a crucial technique for achieving artificial intelligence (AI), has been
successfully applied in many fields. The gradual application of the latest architectures of …

Causal analysis for time series data, in particular estimating individualized treatment effect
(ITE), is a key task in many real world applications, such as finance, retail, healthcare, etc …

With the development of Internet of Things (IoT) systems, precise long-term forecasting
method is requisite for decision makers to evaluate current statuses and formulate future …

Accurate wind speed prediction is crucial for effective wind power grid integration and
energy dispatching. Recent research has explored the combination of decomposition …

Urban time series data forecasting featuring significant contributions to sustainable
development is widely studied as an essential task of the smart city. However, with the …

Wind is one kind of high-efficient, environmentally-friendly, and cost-effective energy source.
Wind power, as one of the largest renewable energy in the world, has been playing a more …