Forecasting Web page views: Methods and observations

Jia Li, Andrew W. Moore

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Web sites must forecast Web page views in order to plan computer resource allocation and estimate upcoming revenue and advertising growth. In this paper, we focus on extracting trends and seasonal patterns from page view series, two dominant factors in the variation of such series. We investigate the Holt-Winters procedure and a state space model for making relatively short-term prediction. It is found that Web page views exhibit strong impulsive changes occasionally. The impulses cause large prediction errors long after their occurrences. A method is developed to identify impulses and to alleviate their damage on prediction. We also develop a long-range trend and season extraction method, namely the Elastic Smooth Season Fitting (ESSF) algorithm, to compute scalable and smooth yearly seasons. ESSF derives the yearly season by minimizing the residual sum of squares under smoothness regularization, a quadratic optimization problem. It is shown that for longterm prediction, ESSF improves accuracy significantly over other methods that ignore the yearly seasonality.

Original languageEnglish (US)
Pages (from-to)2217-2250
Number of pages34
JournalJournal of Machine Learning Research
Volume9
StatePublished - Oct 1 2008

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Forecasting Web page views: Methods and observations'. Together they form a unique fingerprint.

Cite this