TY - GEN
T1 - Hierarchical tree-based sequential event prediction with application in the aviation accident report
AU - Zhao, Xinyu
AU - Yan, Hao
AU - Liu, Yongming
N1 - Funding Information:
ACKNOWLEDGMENT The research reported in this paper was supported by funds from NASA University Leadership Initiative program (Contract No. NNX17AJ86A, Project Officer: Dr. Anupa Bajwa, Principal Investigator: Dr. Yongming Liu) and NSF DMS 1830363. The support is gratefully acknowledged.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/4
Y1 - 2021/4
N2 - Sequential event prediction is a well-studied area and has been widely used in proactive management, recommender systems and healthcare. One major assumption of the existing sequential event prediction methods is that similar event sequence patterns in the historical record will repeat themselves, enabling us to predict future events. However, in reality, the assumption becomes less convincing when we are trying to predict rare or unique sequences. Furthermore, the representation of the event may be complex with hierarchical structures. In this paper, we aim to solve this issue by taking advantage of the multi-level or hierarchical representation of these rare events. We proposed to build a sequential Encoder-Decoder framework to predict the event sequences. More specifically, in the encoding layer, we built a hierarchical embedding representation for the events. In the decoding layer, we first predict the high-level events and the low-level events are generated according to a hierarchical graphical structure. We propose to link the encoding decoding layers with the temporal models for future event prediction. In this article, we further discussed applying the proposed model into the failure event prediction according to the aviation accident reports and have shown improved accuracy and model interpretability.
AB - Sequential event prediction is a well-studied area and has been widely used in proactive management, recommender systems and healthcare. One major assumption of the existing sequential event prediction methods is that similar event sequence patterns in the historical record will repeat themselves, enabling us to predict future events. However, in reality, the assumption becomes less convincing when we are trying to predict rare or unique sequences. Furthermore, the representation of the event may be complex with hierarchical structures. In this paper, we aim to solve this issue by taking advantage of the multi-level or hierarchical representation of these rare events. We proposed to build a sequential Encoder-Decoder framework to predict the event sequences. More specifically, in the encoding layer, we built a hierarchical embedding representation for the events. In the decoding layer, we first predict the high-level events and the low-level events are generated according to a hierarchical graphical structure. We propose to link the encoding decoding layers with the temporal models for future event prediction. In this article, we further discussed applying the proposed model into the failure event prediction according to the aviation accident reports and have shown improved accuracy and model interpretability.
KW - Aviation accident
KW - Embedding
KW - Event prediction
KW - Hierarchical tree structure
KW - Sequential model
UR - http://www.scopus.com/inward/record.url?scp=85112865040&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85112865040&partnerID=8YFLogxK
U2 - 10.1109/ICDE51399.2021.00178
DO - 10.1109/ICDE51399.2021.00178
M3 - Conference contribution
AN - SCOPUS:85112865040
T3 - Proceedings - International Conference on Data Engineering
SP - 1925
EP - 1930
BT - Proceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021
PB - IEEE Computer Society
T2 - 37th IEEE International Conference on Data Engineering, ICDE 2021
Y2 - 19 April 2021 through 22 April 2021
ER -