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Predicting the occurrence of world news events using recurrent neural networks and auto-regressive moving average models

Smith, Emmanuel M.; Smith, Jim; Legg, Philip; Francis, Simon

Authors

Emmanuel M. Smith

Jim Smith James.Smith@uwe.ac.uk
Professor in Interactive Artificial Intelligence

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Dr Phil Legg Phil.Legg@uwe.ac.uk
Associate Professor in Cyber Security

Simon Francis



Contributors

Fei Chao
Editor

Steven Schockaert
Editor

Qingfu Zhang
Editor

Abstract

The ability to predict future states is fundamental for a wide variety of applications, from weather forecasting to stock market analysis. Understanding the related data attributes that can influence changes in time series is a challenging task that is critical for making accurate predictions. One particular application of key interest is understanding the factors that relate to the occurrence of global activities from online world news reports. Being able to understand why particular types of events may occur, such as violence and peace, could play a vital role in better protecting and understanding our global society. In this work, we explore the concept of predicting the occurrence of world news events, making use of Global Database of Events, Language and Tone online news aggregation source. We compare traditional Auto-Regressive Moving Average models with more recent deep learning strategies using Long Short-Term Memory Recurrent Neural Networks. Our results show that the latter are capable of achieving lower error rates. We also discuss how deep learning methods such as Recurrent Neural Networks have the potential for greater capability to incorporate complex associations of data attributes that may impact the occurrence of future events.

Citation

Smith, E. M., Smith, J., Legg, P., & Francis, S. (2017). Predicting the occurrence of world news events using recurrent neural networks and auto-regressive moving average models. In F. Chao, S. Schockaert, & Q. Zhang (Eds.), Advances in Computational Intelligence Systems: UKCI 2017, 191-202. Springer Cham

Conference Name UKCI 2017
Start Date Sep 6, 2017
End Date Sep 8, 2017
Acceptance Date Jun 30, 2017
Publication Date Sep 5, 2017
Print ISSN 2194-5357
Peer Reviewed Peer Reviewed
Volume 650
Pages 191-202
Series Title Advances in Intelligent Systems and Computing
Book Title Advances in Computational Intelligence Systems: UKCI 2017
ISBN 9783319669380
Keywords time series prediction, long short-term memory, autoregressive moving average, global event data
Publisher URL https://doi.org/10.1007/978-3-319-66939-7_16
Additional Information Title of Conference or Conference Proceedings : 17th Annual UK Workshop on Computational Intelligence

This file is under embargo due to copyright reasons.






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