Hybrid models for future event prediction

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

We present a hybrid method to turn off-the-shelf information retrieval (IR) systems into future event predictors. Given a query, a time series model is trained on the publication dates of the retrieved documents to capture trends and periodicity of the associated events. The periodicity of historic data is used to estimate a probabilistic model to predict future bursts. Finally, a hybrid model is obtained by intertwining the probabilistic and the time-series model. Our empirical results on the New York Times corpus show that autocorrelation functions of time-series suffice to classify queries accurately and that our hybrid models lead to more accurate future event predictions than baseline competitors.

Original languageEnglish
Title of host publicationProceedings of the 20th ACM international conference on Information and knowledge management
EditorsBettina Berendt, Arjen de Vries, Wenfei Fan, Craig Macdonald, Iadh Ounis, Ian Ruthven
Number of pages4
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Publication date2011
Pages1981-1984
ISBN (electronic)978-145030717-8
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event20th ACM Conference on Information and Knowledge Management - CIKM '11 - Glasgow, United Kingdom
Duration: 24.10.201128.10.2011
http://www.cikm2011.org/

    Research areas

  • Informatics - Event prediction, Web searches, Forecasting, Information retrieval, Knowledge management, Regression analysis, Time series, World Wide Web
  • Business informatics

DOI

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