Recent efforts have been dedicated to enhancing time series forecasting accuracy by
introducing advanced network architectures and self-supervised pretraining strategies …

Designing effective models for learning time series representations is foundational for time
series analysis. Many previous works have explored time series representation modeling …

The actual collection of tabular data for sharing involves confidentiality and privacy
constraints, leaving the potential risks of machine learning for interventional data analysis …

The inadequacy of labeled datasets for image quality assessment has led to the
development and popularity of self-supervised approaches. However, most existing self …

The automated generation of radiology reports has attracted significant attention in the field
of bioinformatics. Currently, the main limitations of this task include insufficient utilization of …

Real electricity costs, weather, and historical load data are added as new reference data
when analyzing the characteristics of the load change policy to improve the forecasting …

Load current interval prediction (LCIP) plays an increasingly significant role in transmission
lines load demand uncertainty assessment and becomes necessary for power system …

Accurate load forecasting is prime in the electric power industry, while the complexity and
variability of the load data make it a challenging problem. Therefore, the probabilistic load …

In this paper, a PCA-SSA-DBIGRU-Attention multi-factor short-term power load forecasting
model is proposed. Taking a complete account of the influence of meteorological factors …

In this paper, a short-term power load forecasting method based on improved generalised
regression neural network is proposed. The autocorrelation, timing, and periodicity …