Forecasting network traffic: A survey and tutorial with open-source comparative evaluation
This paper presents a review of the literature on network traffic prediction, while also serving
as a tutorial to the topic. We examine works based on autoregressive moving average …
as a tutorial to the topic. We examine works based on autoregressive moving average …
Deep generative model and its applications in efficient wireless network management: A tutorial and case study
With the phenomenal success of diffusion models and ChatGPT, deep generation models
(DGMs) have been experiencing explosive growth. Not limited to content generation, DGMs …
(DGMs) have been experiencing explosive growth. Not limited to content generation, DGMs …
Deep Learning on Network Traffic Prediction: Recent Advances, Analysis, and Future Directions
From the perspective of telecommunications, next-generation networks or beyond 5G will
inevitably face the challenge of a growing number of users and devices. Such growth results …
inevitably face the challenge of a growing number of users and devices. Such growth results …
Identification of encrypted and malicious network traffic based on one-dimensional convolutional neural network
Y Zhou, H Shi, Y Zhao, W Ding, J Han, H Sun… - Journal of Cloud …, 2023 - Springer
The rapid advancement of the Internet has brought a exponential growth in network traffic. At
present, devices deployed at edge nodes process huge amount of data, extract key features …
present, devices deployed at edge nodes process huge amount of data, extract key features …
Anomalous ride-hailing driver detection with deep transfer inverse reinforcement learning
The rapid expansion in group size of online ride-hailing drivers has made anomalous driver
detection become a critical issue, which substantially affects the safety and operation …
detection become a critical issue, which substantially affects the safety and operation …
Internet activity forecasting over 5g billing data using deep learning techniques
Understanding the flexibility of traffic requirements on wireless networks is challenging due
to the high density of mobile devices connected to the network. This has made things more …
to the high density of mobile devices connected to the network. This has made things more …
State Transition Graph-Based Spatial–Temporal Attention Network for Cell-Level Mobile Traffic Prediction
Mobile traffic prediction enables the efficient utilization of network resources and enhances
user experience. In this paper, we propose a state transition graph-based spatial–temporal …
user experience. In this paper, we propose a state transition graph-based spatial–temporal …
A shared multi-scale lightweight convolution generative network for few-shot multivariate time series forecasting
M Zhang, L Sun, J Yang, Y Zou - Applied Soft Computing, 2024 - Elsevier
Time series forecasting is an important time series data mining technique. Among them,
multivariate time series (MTS) forecasting has received extensive attention in many fields …
multivariate time series (MTS) forecasting has received extensive attention in many fields …
AGENDA: Predicting Trip Purposes with A New Graph Embedding Network and Active Domain Adaptation
Trip purpose is a meaningful aspect of travel behaviour for the understanding of urban
mobility. However, it is non-trivial to automatically obtain trip purposes. On one hand, trip …
mobility. However, it is non-trivial to automatically obtain trip purposes. On one hand, trip …
TransMUSE: Transferable traffic prediction in multi-service edge networks
The Covid-19 pandemic has forced the workforce to switch to working from home, which has
put significant burdens on the management of broadband networks and called for intelligent …
put significant burdens on the management of broadband networks and called for intelligent …