Markov-Based Time Series Modeling Framework for Traffic-Network State Prediction under Various External Conditions

2020 ◽  
Vol 146 (6) ◽  
pp. 04020042
Author(s):  
Tao Li ◽  
Jiaqi Ma ◽  
Changju Lee
Keyword(s):  
Time Series ◽  
Traffic Network ◽  
Time Series Modeling ◽  
Modeling Framework ◽  
Network State ◽  
State Prediction ◽  
External Conditions

scholarly journals Road traffic network state prediction based on a generative adversarial network

2020 ◽  
Vol 14 (10) ◽  
pp. 1286-1294
Author(s):  
Dongwei Xu ◽  
Peng Peng ◽  
Chenchen Wei ◽  
Defeng He ◽  
Qi Xuan
Keyword(s):  
Road Traffic ◽  
Traffic Network ◽  
Generative Adversarial Network ◽  
Network State ◽  
Adversarial Network ◽  
State Prediction

Meta-Analysis and Time Series Modeling Allow a Systematic Review of Primary HIV-1 Drug-Resistant Prevalence in Latin America and Caribbean

2015 ◽  
Vol 13 (2) ◽  
pp. 125-142 ◽  
Author(s):  
Antonio Coelho ◽  
Ronald Moura ◽  
Ronaldo Silva ◽  
Anselmo Kamada ◽  
Rafael Guimaraes ◽  
...  
Keyword(s):  
Systematic Review ◽  
Time Series ◽  
Latin America ◽  
Meta Analysis ◽  
Drug Resistant ◽  
Time Series Modeling ◽  
Latin America And Caribbean ◽  
Hiv 1

scholarly journals Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

2021 ◽  
Vol 48 (4) ◽  
pp. 37-40
Author(s):  
Nikolas Wehner ◽  
Michael Seufert ◽  
Joshua Schuler ◽  
Sarah Wassermann ◽  
Pedro Casas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Quality Of Experience ◽  
Unique Feature ◽  
Time Series Modeling ◽  
Web Browsing ◽  
Large Dataset ◽  
Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

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