Modeling and Predicting Temporal Patterns of Web Content Changes

The technologies aimed at Web content discovery, retrieval and management face the compelling need of coping with its highly dynamic nature coupled with complex user interactions. This paper analyzes the temporal patterns of the content changes of three major news websites with the objective of\r\nmodeling and predicting their dynamics. It has been observed that changes are characterized by a time\r\ndependent behavior with large\r\nfluctuations and significant differences across hours and days. To explain this behavior, we represent the change patterns as time series. The trend and seasonal components of\r\nthe observed time series capture the weekly and daily periodicity, whereas the irregular components\r\ntake into account the remaining\r\nfluctuations. Models based on trigonometric polynomials and ARMA\r\ncomponents accurately reproduce the dynamics of the empirical change patterns and provide extra-\r\npolations into the future to be used for forecasting.