Hybrid models for future event prediction

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Standard

Hybrid models for future event prediction. / Amodeo, Giuseppe; Blanco, Roi; Brefeld, Ulf.
Proceedings of the 20th ACM international conference on Information and knowledge management. Hrsg. / Bettina Berendt; Arjen de Vries; Wenfei Fan; Craig Macdonald; Iadh Ounis; Ian Ruthven. New York: Association for Computing Machinery, Inc, 2011. S. 1981-1984.

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Harvard

Amodeo, G, Blanco, R & Brefeld, U 2011, Hybrid models for future event prediction. in B Berendt, A de Vries, W Fan, C Macdonald, I Ounis & I Ruthven (Hrsg.), Proceedings of the 20th ACM international conference on Information and knowledge management. Association for Computing Machinery, Inc, New York, S. 1981-1984, 20th ACM Conference on Information and Knowledge Management - CIKM '11, Glasgow, Großbritannien / Vereinigtes Königreich, 24.10.11. https://doi.org/10.1145/2063576.2063870

APA

Amodeo, G., Blanco, R., & Brefeld, U. (2011). Hybrid models for future event prediction. In B. Berendt, A. de Vries, W. Fan, C. Macdonald, I. Ounis, & I. Ruthven (Hrsg.), Proceedings of the 20th ACM international conference on Information and knowledge management (S. 1981-1984). Association for Computing Machinery, Inc. https://doi.org/10.1145/2063576.2063870

Vancouver

Amodeo G, Blanco R, Brefeld U. Hybrid models for future event prediction. in Berendt B, de Vries A, Fan W, Macdonald C, Ounis I, Ruthven I, Hrsg., Proceedings of the 20th ACM international conference on Information and knowledge management. New York: Association for Computing Machinery, Inc. 2011. S. 1981-1984 doi: 10.1145/2063576.2063870

Bibtex

@inbook{038827f98b584417b80efd43a93e5235,
title = "Hybrid models for future event prediction",
abstract = "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.",
keywords = "Informatics, Event prediction, Web searches, Forecasting, Information retrieval, Knowledge management, Regression analysis, Time series, World Wide Web, Business informatics",
author = "Giuseppe Amodeo and Roi Blanco and Ulf Brefeld",
year = "2011",
doi = "10.1145/2063576.2063870",
language = "English",
pages = "1981--1984",
editor = "Berendt, {Bettina } and {de Vries}, Arjen and Wenfei Fan and Craig Macdonald and Iadh Ounis and Ian Ruthven",
booktitle = "Proceedings of the 20th ACM international conference on Information and knowledge management",
publisher = "Association for Computing Machinery, Inc",
address = "United States",
note = "20th ACM Conference on Information and Knowledge Management - CIKM '11, CIKM '11 ; Conference date: 24-10-2011 Through 28-10-2011",
url = "http://www.cikm2011.org/",

}

RIS

TY - CHAP

T1 - Hybrid models for future event prediction

AU - Amodeo, Giuseppe

AU - Blanco, Roi

AU - Brefeld, Ulf

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - Informatics

KW - Event prediction

KW - Web searches

KW - Forecasting

KW - Information retrieval

KW - Knowledge management

KW - Regression analysis

KW - Time series

KW - World Wide Web

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=83055161561&partnerID=8YFLogxK

U2 - 10.1145/2063576.2063870

DO - 10.1145/2063576.2063870

M3 - Article in conference proceedings

AN - SCOPUS:83055161561

SP - 1981

EP - 1984

BT - Proceedings of the 20th ACM international conference on Information and knowledge management

A2 - Berendt, Bettina

A2 - de Vries, Arjen

A2 - Fan, Wenfei

A2 - Macdonald, Craig

A2 - Ounis, Iadh

A2 - Ruthven, Ian

PB - Association for Computing Machinery, Inc

CY - New York

T2 - 20th ACM Conference on Information and Knowledge Management - CIKM '11

Y2 - 24 October 2011 through 28 October 2011

ER -

DOI

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