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 …

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

Multivariate time series long-term prediction, which aims to predict the change of data in a
long time, can provide references for decision-making. Although transformer-based models …

Wind energy has attracted more and more attention due to its sustainability and pollution-
free nature. As wind energy is highly dependent on wind speed, wind speed forecasting is of …

Although time series prediction models based on Transformer architecture have achieved
significant advances, concerns have arisen regarding their performance with non-stationary …

With the advancement of deep learning algorithms and the growing availability of
computational power, deep learning-based forecasting methods have gained significant …

The growing number of mobile devices over the past few years brings a large amount of
personal information, which needs to be properly protected. As a result, several mobile …

Long-term time series forecasting (LTSF) aims to predict future values for a long time based
on historical data. The period term is an essential component of the time series, which is …

In the domain of multivariate forecasting, transformer models stand out as powerful
apparatus, displaying exceptional capabilities in handling messy datasets from real-world …

The temperature within the sintering furnace is a decisive factor influencing the quality of the
sintered ore in the iron ore sintering process. In practical operations, the temperature at the …