6 papers
VFEM: Visual Feature Empowered Multivariate Time Series Forecasting with Cross-Modal Fusion
Yanlong Wang, Hang Yu, Jian Xu +7
Large time series foundation models often adopt channel-independent architectures to handle varying data dimensions, but this design ignores crucial cross-channel dependencies. Mea…
PHGNet: Prototype-Guided Hypergraph Construction for Heterogeneous Spatiotemporal Forecasting
Ruiwen Gu, Yahao Liu, Zhenyu Liu +2
As a core task in intelligent transportation systems, traffic forecasting plays a critical role in urban traffic management. Accurate traffic forecasting relies on modeling complex…
ADMFormer: An Adaptive-Decomposition Transformer with Time-Varying Masked Spatial Attention for Traffic Forecasting
Ruiwen Gu, Qitai Tan, Yahao Liu +1
Accurate traffic forecasting is essential for intelligent transportation systems, supporting a wide range of real-world applications. However, it remains challenging due to two key…
ResLearn: Transformer-based Residual Learning for Metaverse Network Traffic Prediction
Yoga Suhas Kuruba Manjunath, Mathew Szymanowski, Austin Wissborn +3
Our work proposes a comprehensive solution for predicting Metaverse network traffic, addressing the growing demand for intelligent resource management in eXtended Reality (XR) serv…
Discern-XR: An Online Classifier for Metaverse Network Traffic
Yoga Suhas Kuruba Manjunath, Austin Wissborn, Mathew Szymanowski +3
In this paper, we design an exclusive Metaverse network traffic classifier, named Discern-XR, to help Internet service providers (ISP) and router manufacturers enhance the quality…
Time-Distributed Feature Learning for Internet of Things Network Traffic Classification
Yoga Suhas Kuruba Manjunath, Sihao Zhao, Xiao-Ping Zhang +1
Deep learning-based network traffic classification (NTC) techniques, including conventional and class-of-service (CoS) classifiers, are a popular tool that aids in the quality of s…