Abstract
The goal of Crystal Cube is to create an automated capability for the prediction of disruptive events. In this paper we present initial prediction results on six prediction categories previously shown to be of interest in the literature. In particular, we compare the performance of static classification models, often used in previous work for these prediction tasks, with a gated recurrent unit sequence model that has the ability to retain information over long periods of time for the classification of sequence data. Our results show that the sequence model is comparable in performance to the best performing static model (the random forest), and that more work is needed to classify highly dynamic prediction categories with high probability.
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Parrish, N.H., Buczak, A.L., Zook, J.T., Howard, J.P., Ellison, B.J., Baugher, B.D. (2019). Crystal Cube: Multidisciplinary Approach to Disruptive Events Prediction. In: Kantola, J.I., Nazir, S., Barath, T. (eds) Advances in Human Factors, Business Management and Society. AHFE 2018. Advances in Intelligent Systems and Computing, vol 783. Springer, Cham. https://doi.org/10.1007/978-3-319-94709-9_56
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DOI: https://doi.org/10.1007/978-3-319-94709-9_56
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