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Prediction Analysis on Web Traffic Data Using Time Series Modeling, RNN and Ensembling Techniques

  • Naveena Reddy MettuEmail author
  • T. SasikalaEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)

Abstract

In the present day web traffic holds the major segment of Internet based traffic. This information can be retrieved by building number of visits for a particular page by number of callers which helps to know the popularity of the webpage. So predicting the web traffic for further can help to maintain unforeseen traffic load there by deducting the Slashdot effect and Flash crowd effects. In this paper we mainly pivot on forecasting the Wikipedia web traffic using Ensembling technique called Boosting – AdaBoostRegressor, RNN technique LSTM and Time series modelling technique ARIMA. Further achievement of best technique of the models has been examined.

Keywords

Prediction Time series modelling Ensembling Forecasting Moving average Auto regressive RNN LSTM 

References

  1. 1.
    Box, G.E.P., Jenkins, G.M.: Time Series Analysis: Forecasting and Control, 2nd edn. Holden-Day, San Francisco (1976)zbMATHGoogle Scholar
  2. 2.
    Pai, P.-F., Hong, W.C.: Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electr. Power Syst. Res. 74, 417–425 (2005)CrossRefGoogle Scholar
  3. 3.
    Ren, X., Chen, X.: Discovery and dynamic prediction of user’s ınterest based on ARIMA. In: 2017 PICMET, pp. 1–8 (2017)Google Scholar
  4. 4.
    Nichiforov, C., Stamatescu, I., Făgărăşan, I., Stamatescu, G.: Energy consumption forecasting using ARIMA and neural network models. In: 2017 5th ISEEE, pp. 1–4 (2017)Google Scholar
  5. 5.
    Wang, B., Zhu, X., He, Q., Gu, G.: The forecast on the customers of the member point platform built on the blockchain technology by ARIMA and LSTM. In: 2018 IEEE 3rd ICCCBDA, pp. 589–593 (2018)Google Scholar
  6. 6.
    Zeng, J., Zhang, L., Shi, G., Liu, T., Lin, K.: An ARIMA based real-time monitoring and warning algorithm for the anomaly detection. In: 2017 IEEE 23rd ICPADS, pp. 469–476 (2017)Google Scholar
  7. 7.
    Babu, K., Vasavi, S., Nagarjuna, K.: Framework for predictive analytics as a service using ensemble model. In: 2017 IEEE 7th IACC, pp. 121–128 (2017)Google Scholar
  8. 8.
    Raj, J.A.S., Fernando, L., Raj, Y.S.: Predictive analytics on facebook data. In: 2017 (WCCCT), pp. 93–96 (2017)Google Scholar
  9. 9.
    Durka, P., Pastorekov, S.: ARIMA vs. ARIMAX – which approach is better to analyze and forecast macroeconomic time series. In: Proceedings of 30th International Conference, pp. 136–140, September 2012Google Scholar
  10. 10.
    Gupta, D., Khare, S., Aggarwal, A.: A method to predict diagnostic codes for chronic diseases using machine learning techniques. In: 2016 ICCCA, pp. 281–287 (2016)Google Scholar
  11. 11.
    Selvin, S., Vinayakumar, R., Gopalakrishnan, E.A., Menon, V.K., Soman, K.P.: Stock price prediction using LSTM, RNN and CNN-sliding window model. In: 2017 (ICACCI), pp. 1643–1647 (2017)Google Scholar
  12. 12.
    Sathyadevan, S., Devan M.S., Gangadharan, S.: Crime analysis and prediction using data mining. In: 2014 First ICNSC2014, pp. 406–412 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAmrita School of EngineeringBengaluruIndia
  2. 2.Amrita Vishwa VidyapeethamCoimbatoreIndia

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