Forecasting Web Page Views: Methods and Observations
Venue
JMLR, vol. 9(Oct) (2008), pp. 2217-2250
Publication Year
2008
Authors
Jia Li, Andrew Moore
BibTeX
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
long-term prediction, ESSF improves accuracy significantly over other methods that
ignore the yearly seasonality.
