A Methodological Approach for Time Series Analysis and Forecasting of Web Dynamics

  • Maria Carla CalzarossaEmail author
  • Marco L. Della Vedova
  • Luisa Massari
  • Giuseppe Nebbione
  • Daniele Tessera
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11610)


The web is a complex information ecosystem that provides a large variety of content changing over time as a consequence of the combined effects of management policies, user interactions and external events. These highly dynamic scenarios challenge technologies dealing with discovery, management and retrieval of web content. In this paper, we address the problem of modeling and predicting web dynamics in the framework of time series analysis and forecasting. We present a general methodological approach that allows the identification of the patterns describing the behavior of the time series, the formulation of suitable models and the use of these models for predicting the future behavior. Moreover, to improve the forecasts, we propose a method for detecting and modeling the spiky patterns that might be present in a time series. To test our methodological approach, we analyze the temporal patterns of page uploads of the Reuters news agency website over one year. We discover that the upload process is characterized by a diurnal behavior and by a much larger number of uploads during weekdays with respect to weekend days. Moreover, we identify several sudden spikes and a daily periodicity. The overall model of the upload process – obtained as a superposition of the models of its individual components – accurately fits the data, including most of the spikes.


Web dynamics Temporal patterns Time series analysis Forecasting Performance modeling Search engines ARMA models 


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Maria Carla Calzarossa
    • 1
    Email author
  • Marco L. Della Vedova
    • 2
  • Luisa Massari
    • 1
  • Giuseppe Nebbione
    • 1
  • Daniele Tessera
    • 2
  1. 1.Dipartimento di Ingegneria Industriale e dell’InformazioneUniversità di PaviaPaviaItaly
  2. 2.Dipartimento di Matematica e FisicaUniversità Cattolica del Sacro CuoreBresciaItaly

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