Comparative study on the time series forecasting of web traffic based on statistical model and Generative Adversarial model

2020 ◽  
pp. 106467
Author(s):  
Kun Zhou ◽  
Wenyong Wang ◽  
Lisheng Huang ◽  
Baoyang Liu
2020 ◽  
Vol 32 ◽  
pp. 03017
Author(s):  
Tejas Shelatkar ◽  
Stephen Tondale ◽  
Swaraj Yadav ◽  
Sheetal Ahir

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;


2020 ◽  
Vol 138 ◽  
pp. 104461 ◽  
Author(s):  
Shivam Bhardwaj ◽  
E. Chandrasekhar ◽  
Priyanka Padiyar ◽  
Vikram M. Gadre

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