Predicting the Occurrence of World News Events Using Recurrent Neural Networks and Auto-Regressive Moving Average Models

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.