Static Seeding and Clustering of LSTM Embeddings to Learn From Loosely Time-Decoupled Events

Humans learn from the occurrence of events at different places and times to predict similar trajectories of events. We define loosely decoupled time (LDT) phenomena as two or more events that could occur in different places and across different timelines but share similarities in the nature of the e...

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Bibliographic Details

Published in:	IEEE access Vol. 11; pp. 64219 - 64227
Main Authors:	Manasseh, Christian G., Veliche, Razvan, Bennett, Jared, Clouse, Hamilton Scott
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithm design and analysis artificial intelligence Clustering COVID-19 Data models Diseases Hidden Markov models Multivariate analysis numerical algorithms and problems Predictive models Recurrent neural networks statistical methods Time series Time series analysis Training
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
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