Visual analytics for machine learning has recently evolved as one of the most exciting areas
in the field of visualization. To better identify which research topics are promising and to …

The use and creation of machine‐learning‐based solutions to solve problems or reduce
their computational costs are becoming increasingly widespread in many domains. Deep …

Long short‐term memory (LSTM) networks represent one of the most prevalent deep
learning (DL) architectures in current hydrological modeling, but they remain black boxes …

Developing effective visual analytics systems demands care in characterization of domain
problems and integration of visualization techniques and computational models. Urban …

The past decade has witnessed a plethora of works that leverage the power of visualization
(VIS) to interpret machine learning (ML) models. The corresponding research topic, VIS4ML …

Deep learning methods are being increasingly used for urban traffic prediction where
spatiotemporal traffic data is aggregated into sequentially organized matrices that are then …

With deep learning (DL) outperforming conventional methods for different tasks, much effort
has been devoted to utilizing DL in various domains. Researchers and developers in the …

Deep learning (DL) approaches are being increasingly used for time-series forecasting, with
many efforts devoted to designing complex DL models. Recent studies have shown that the …

Time-series forecasting contributes crucial information to industrial and institutional decision-
making with multivariate time-series input. Although various models have been developed to …

Time series data introduces two key challenges for explainability methods: firstly,
observations of the same feature over subsequent time steps are not independent, and …