A methodological approach for time series analysis and forecasting of web dynamics

Marco Luigi Della Vedova, Daniele Tessera, Luisa Massari, Maria Carla Calzarossa, Giuseppe Nebbione

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

3 Citazioni (Scopus)

Abstract

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.
Lingua originaleEnglish
pagine (da-a)128-143
Numero di pagine16
RivistaTRANSACTIONS ON COMPUTATIONAL COLLECTIVE INTELLIGENCE
Volume11610
DOI
Stato di pubblicazionePubblicato - 2019

Keywords

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

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