This is a valuable study on how past sensory experiences shape perception across multiple time scales. Using a behavioural task and reanalysed EEG data, the authors identify two unifying mechanisms ...

While traditional methods primarily focus on modeling time series data at a single temporal scale and achieve notable results, they often overlook dependencies across multiple scales. Furthermore, the ...

Energy demand in smart grids is highly variable and influenced by external factors, making accurate forecasting challenging. While deep learning models excel in time-series forecasting, their ability ...

Multidimensional time series (MTS) has the unique characteristics of multidimensionality and multifeature, so it becomes particularly important when choosing a prediction model. Therefore, this ...

This review aims to provide a cross-domain synthesis of how AEs, ViTs, and their hybrid forms are applied in unsupervised and semi-supervised settings for time-series signal analysis. It identifies ...

A new computational method can identify how cause-and-effect relationships ebb and flow over time in dynamic real-life ...

What I want are ways to handle graphs, where a single time series may have multiple different units. Consider for example a metric that shows a temperature or one that shows network traffic or disc IO ...