The comparative analysis of SARIMA, Facebook Prophet, and LSTM for road traffic injury prediction in Northeast China

OBJECTIVE: This cross-sectional research aims to develop reliable predictive short-term prediction models to predict the number of RTIs in Northeast China through comparative studies. METHODOLOGY: Seasonal auto-regressive integrated moving average (SARIMA), Long Short-Term Memory (LSTM), and Faceboo...

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Detalles Bibliográficos
Autores principales: Feng, Tianyu, Zheng, Zhou, Xu, Jiaying, Liu, Minghui, Li, Ming, Jia, Huanhuan, Yu, Xihe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Public Health
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354624/
https://www.ncbi.nlm.nih.gov/pubmed/35937210
http://dx.doi.org/10.3389/fpubh.2022.946563

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354624/
https://www.ncbi.nlm.nih.gov/pubmed/35937210
http://dx.doi.org/10.3389/fpubh.2022.946563

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