Time series classification by Euclidean distance-based visibility graph
The analysis and discrimination of time series data has important practical significance.
Currently, transforming the time series data into networks through visibility graph (VG) …
Currently, transforming the time series data into networks through visibility graph (VG) …
Gnn-geo: A graph neural network-based fine-grained ip geolocation framework
Rule-based fine-grained IP geolocation methods are hard to generalize in computer
networks which do not follow hypothetical rules. Recently, deep learning methods, like multi …
networks which do not follow hypothetical rules. Recently, deep learning methods, like multi …
Automatic modulation classification using graph convolutional neural networks for time-frequency representation
Recognition of the radio signal's modulating scheme is becoming increasingly important in
civil and military applications. It can potentially alleviate the electromagnetic signal …
civil and military applications. It can potentially alleviate the electromagnetic signal …
Multiple time series forecasting with Graph Neural Networks
A Lombardi - amslaurea.unibo.it
Time series forecasting aims to predict future values to support organizations making
strategic decisions. This problem has been studied for decades due to its relevance in …
strategic decisions. This problem has been studied for decades due to its relevance in …
[PDF][PDF] ALMA MATER STUDIORUM UNIVERSITA DI BOLOGNA
A Lombardi, Z Kiziltan - amslaurea.unibo.it
Time series forecasting aims to predict future values to support organizations making
strategic decisions. This problem has been studied for decades due to its relevance in …
strategic decisions. This problem has been studied for decades due to its relevance in …