Graph Recurrent Neural Networks (GRNN) excel in time-series prediction by modeling complicated non-linear relationships among time-series. However, most GRNN models target small datasets that only have tens of time-series or hundreds of time-...$\mathrm{}$$\mathrm{}$$\mathrm{}$$\mathrm{}$

Multivariate Time series data forecasting (MTSF) is the assignment of forecasting future estimates of a particular series employing historic data. Lately, this work has enticed the focus of machine and deep learning researchers to tackle the ...

Probabilistic time-series forecasting enables reliable decision making across many domains. Most forecasting problems have diverse sources of data containing multiple modalities and structures. Leveraging information from these data sources for accurate ...

In various application domains/sectors, data collected from the respective industries are complemented with open data providing added value to the overall analysis and decision making process. Open data refer to weather data, transportation ...