Multistep traffic forecasting by dynamic graph convolution: Interpretations of real-time spatial correlations

Web Traffic Time Series Forecasting using ARIMA and LSTM RNN

ITM Web of Conferences ◽

10.1051/itmconf/20203203017 ◽

2020 ◽

Vol 32 ◽

pp. 03017

Author(s):

Tejas Shelatkar ◽

Stephen Tondale ◽

Swaraj Yadav ◽

Sheetal Ahir

Keyword(s):

Time Series ◽

Real Time ◽

Time Series Forecasting ◽

Web Traffic ◽

Time Data ◽

Traffic Forecasting ◽

Traffic Time Series ◽

Breakdown Time ◽

Server Breakdown ◽

Multiple Domains

Nowadays, web traffic forecasting is a major problem as this can cause setbacks to the workings of major websites. Time-series forecasting has been a hot topic for research. Predicting future time series values is one of the most difficult problems in the industry. The time series field encompasses many different issues, ranging from inference and analysis to forecasting and classification. Forecasting the network traffic and displaying it in a dashboard that updates in real-time would be the most efficient way to convey the information. Creating a Dashboard would help in monitoring and analyzing real-time data. In this day and age, we are too dependent on Google server but if we want to host a server for large users we could have predicted the number of users from previous years to avoid server breakdown. Time Series forecasting is crucial to multiple domains. ARIMA; LSTM RNN; web traffic; prediction;time series;

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A resilient and distributed near real-time traffic forecasting application for Fog computing environments

Future Generation Computer Systems ◽

10.1016/j.future.2018.05.013 ◽

2018 ◽

Vol 87 ◽

pp. 198-212 ◽

Cited By ~ 15

Author(s):

Juan Luis Pérez ◽

Alberto Gutierrez-Torre ◽

Josep Ll. Berral ◽

David Carrera

Keyword(s):

Real Time ◽

Fog Computing ◽

Traffic Forecasting ◽

Real Time Traffic ◽

Computing Environments

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SDP: Scalable Real-time Dynamic Graph Partitioner

IEEE Transactions on Services Computing ◽

10.1109/tsc.2021.3137932 ◽

2021 ◽

pp. 1-1

Author(s):

Md Anwarul Kaium Patwary ◽

Saurabh Garg ◽

Sudheer Kumar Battula ◽

Byeong Ho Kang

Keyword(s):

Real Time ◽

Dynamic Graph ◽

Time Dynamic

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A Scalable Approach for Short-Term Predictions of Link Traffic Flow by Online Association of Clustering Profiles

Transport and Telecommunication Journal ◽

10.2478/ttj-2020-0009 ◽

2020 ◽

Vol 21 (2) ◽

pp. 119-124

Author(s):

Alessandro Attanasi ◽

Marco Pezzulla ◽

Luca Simi ◽

Lorenzo Meschini ◽

Guido Gentile

Keyword(s):

Real Time ◽

Traffic Management ◽

Time Window ◽

Management Control ◽

Spatial Correlations ◽

Time Data ◽

Short Term ◽

Data Set ◽

Affinity Propagation Algorithm ◽

Short Term Prediction

AbstractShort-term prediction of traffic flows is an important topic for any traffic management control room. The large availability of real-time data raises not only the expectations for high accuracy of the forecast methodology, but also the requirements for fast computing performances. The proposed approach is based on a real-time association of the latest data received from a sensor to the representative daily profile of one among the clusters that are built offline based on an historical data set using Affinity Propagation algorithm. High scalability is achieved ignoring spatial correlations among different sensors, and for each of them an independent model is built-up. Therefore, each sensor has its own clusters of profiles with their representatives; during the short-term forecast operation the most similar representative is selected by looking at the last data received in a specified time window and the proposed forecast corresponds to the values of the cluster representative.

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