Network Traffic Prediction Using Long Short-Term Memory

2020 ◽  
Author(s):  
Shyam Nihale ◽  
Shantanu Sharma ◽  
Lokesh Parashar ◽  
Upendra Singh
Keyword(s):  
Network Traffic ◽  
Short Term Memory ◽  
Traffic Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals An LSTM-Based Method with Attention Mechanism for Travel Time Prediction

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 861 ◽  
Author(s):  
Xiangdong Ran ◽  
Zhiguang Shan ◽  
Yufei Fang ◽  
Chuang Lin
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Traffic Prediction ◽  
Travel Time Prediction ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Departure Time ◽  
Long Short Term Memory

Traffic prediction is based on modeling the complex non-linear spatiotemporal traffic dynamics in road network. In recent years, Long Short-Term Memory has been applied to traffic prediction, achieving better performance. The existing Long Short-Term Memory methods for traffic prediction have two drawbacks: they do not use the departure time through the links for traffic prediction, and the way of modeling long-term dependence in time series is not direct in terms of traffic prediction. Attention mechanism is implemented by constructing a neural network according to its task and has recently demonstrated success in a wide range of tasks. In this paper, we propose an Long Short-Term Memory-based method with attention mechanism for travel time prediction. We present the proposed model in a tree structure. The proposed model substitutes a tree structure with attention mechanism for the unfold way of standard Long Short-Term Memory to construct the depth of Long Short-Term Memory and modeling long-term dependence. The attention mechanism is over the output layer of each Long Short-Term Memory unit. The departure time is used as the aspect of the attention mechanism and the attention mechanism integrates departure time into the proposed model. We use AdaGrad method for training the proposed model. Based on the datasets provided by Highways England, the experimental results show that the proposed model can achieve better accuracy than the Long Short-Term Memory and other baseline methods. The case study suggests that the departure time is effectively employed by using attention mechanism.

Classifications of Network Traffic Using Long Short-term Memory

2021 ◽  
Author(s):  
Dmitriy Starodubtsev ◽  
Vitalii Varkentin ◽  
Aleksey V. Minbaleev
Keyword(s):  
Network Traffic ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

A method for constructing a network traffic classifier based on deep learning

2021 ◽  
Author(s):  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Traffic ◽  
Short Term Memory ◽  
Short Term ◽  
Raw Data ◽  
Term Memory ◽  
High Classification Accuracy ◽  
Data Flows ◽  
Long Short Term Memory

An overview and analysis of methods for constructing of network traffic classifiers is given and the advantage of deep learning methods is shown. Based on a comparative analysis, the methods of deep learning with a teacher is selected. A method based on the use of a multilayer neural network of long short-term memory (LSTM) is considered. The structure of a deep network, at the input of which raw data flows fed, divided into sessions is created. Based on the selected classes of applications, it is experimentally proven that the developed neural network of long short-term memory, to the input of which raw data is supplied, allows to obtain a high classification accuracy. Keywords network traffic; deep learning; long short-term memory neural network; raw data

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