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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Veröffentlicht in:	IEEE access Jg. 11; S. 64219 - 64227
Hauptverfasser:	Manasseh, Christian G., Veliche, Razvan, Bennett, Jared, Clouse, Hamilton Scott
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
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