Hybrid models for future event prediction

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. Categories and Subject Descriptors H.3.0 [Information Storage and Retrieval]: General; H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing; H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms Theory, Experimentation Keywords Fu...

• Type: Journal
• Year: 2011
• Where: CIKM
• Authors: Giuseppe Amodeo, Roi Blanco, Ulf Brefeld
• Keywords: CIKM 2011, Hybrid Models, Information Storage And Retrieval, Information Technology, Time Series Model