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 …

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 …

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 …

Kolmogorov-Arnold Networks (KAN) is a groundbreaking model recently proposed by the
MIT team, representing a revolutionary approach with the potential to be a game-changer in …

Dynamic concepts in time series are crucial for understanding complex systems such as
financial markets, healthcare, and online activity logs. These concepts help reveal structures …

Precise energy load forecasting in residential households is crucial for mitigating carbon
emissions and enhancing energy efficiency; indeed, accurate forecasting enables utility …

Outbreaks of infectious diseases have caused tremendous human suffering and
incalculable economic losses, and infectious diseases are a global public health problem …

In practical scenarios, time series forecasting necessitates not only accuracy but also
efficiency. Consequently, the exploration of model architectures remains a perennially …

Recently, channel-independent methods have achieved state-of-the-art performance in
multivariate time series (MTS) forecasting. Despite reducing overfitting risks, these methods …

Recent advances in deep learning have led to the development of numerous models for
Long-term Time Series Forecasting (LTSF). However, most approaches still struggle to …