Automatic detection of trends in time-stamped sequences: an evolutionary approach

This paper presents an evolutionary algorithm for modeling the arrival dates in time-stamped data sequences such as newscasts, e-mails, IRC conversations, scientific journal articles or weblog postings. These models are applied to the detection of buzz (i.e. terms that occur with a higher-than-normal frequency) in them, which has attracted a lot of interest in the online world with the increasing number of periodic content producers. That is why in this paper we have used this kind of online sequences to test our system, though it is also valid for other types of event sequences. The algorithm assigns frequencies (number of events per time unit) to time intervals so that it produces an optimal fit to the data. The optimization procedure is a trade off between accurately fitting the data and avoiding too many frequency changes, thus overcoming the noise inherent in these sequences. This process has been traditionally performed using dynamic programming algorithms, which are limited b...

• Type: Journal
• Year: 2010
• Where: SOCO
• Authors: Lourdes Araujo, Juan Julián Merelo Guervós
• Keywords: Algorithm, Dynamic Programming Algorithms, Evolutionary Algorithm, SOCO 2010, Software Engineering