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时间序列分析用于预测泰国两家大象医院的月度工作量

Worapong Kosaruk^1,2, Veerasak Punyapornwithaya¹, Chatchote Thitaram^1,2

¹Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, Thailand.

|December 30, 2025

概括

准确的象保健工作量预测对于兽医医院的资源管理至关重要. 这项研究发现,不同的时间序列模型最能预测两个泰国大象医院的月度病例数量,从而使危物种护理的规划更好.

科学领域:

兽医医学兽医医学兽医医学
野生动物保护野生动物保护
数据科学和数据分析

背景情况:

有效的资源管理对于大象的医疗保健至关重要,需要准确的工作负载预测.
由于无法预测的患者数量,兽医医院面临人员,设备和药物短缺的挑战.
本研究探讨了预测分析在管理专门野生动物护理资源方面的需求.

研究的目的:

评估和比较六个时间序列预测模型的性能,以预测每月的大象病例数量.
为了确定两个主要的大象医院中的每一个具有不同的数据特征的最准确的预测模型.
为在危物种兽医护理中提供数据驱动决策的框架.

主要方法:

分析国家大象研究所 (NEI) 和畜牧发展部 (DLD) 医院的历史月入院数据.
对六种时间序列预测模型的评估:ARIMA/SARIMA,ETS,TBATS,NNTR,Prophet和XGBoost.
使用历史数据和2025年的预测对模型准确性的比较.

主要成果:

指数式平滑 (ETS) 模型显示了NEI医院的最高准确性,捕捉了稳定和季节性模式.
神经网络时间序列回归 (NNTR) 模型在DLD医院表现最好,处理不规则的病例量波动.
2025年的预测表明,NEI的案件量持续增长,DLD的案件数量正在下降,这凸显了预期性资源规划的必要性.

结论:

多模型时间序列预测可以从稀疏的野生动物医院数据中获得可操作的见解.
不同的预测模型对于不同的案例负载模式是最佳的,需要量身定制的方法.
实施预测分析提供了一个可扩展的框架,以提高资源管理和决策在危物种的医疗保健.

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