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使用多变量ARMAX和NLARX模型预测土耳其的职业事故
Selcan Kaplanvural1, Eren Tosyalı2, İsmail Ekmekçi3
1Vocational School of Health Services, Occupational Health and Safety, Istanbul Bilgi University, 34387, Istanbul, Turkey. selcan.cicek@bilgi.edu.tr.
Scientific reports
|January 19, 2026
概括
使用外源输入自动回归移动平均值 (ARMAX) 和外源输入非线性自动回归 (NLARX) 模型预测土耳其的职业事故显示,ARMAX提供了更强大的预测. 这有助于制定有效的安全策略.
科学领域:
- 职业安全与健康问题 职业安全与健康问题
- 计量经济学和统计建模.
- 时间序列分析时间序列分析.
背景情况:
- 职业事故在土耳其带来了重大的社会和经济挑战.
- 了解事故趋势对于有效的预防策略至关重要.
研究的目的:
- 在土耳其使用ARMAX和NLARX模型预测未来的职业事故.
- 为了比较线性和非线性时间序列模型对事故数据的预测性能.
主要方法:
- 使用的自动回归移动平均线与外源输入 (ARMAX) 和非线性自动回归与外源输入 (NLARX) 模型.
- 利用了四个事故相关人群的官方保险记录,由于数据限制,专注于内源动态.
- 在训练,测试和完整数据集中使用正常化平均平方误差 (NMSE) 评估模型性能.
主要成果:
- 在大多数人群中,ARMAX模型表现出卓越的预测准确性,特别是在[公式:见文本]和[公式:见文本]中.
- 在测试期间,NLARX模型对[公式:参见文本]的表现最好,但对[公式:参见文本]的预测错误更高.
- 基于显著性的分析揭示了明显的线性动态,[公式:参见文本]种群表现出更主导的线性效应.
结论:
- 亚马克斯模型为模拟土耳其的职业事故趋势提供了一种更强大,更一致的概括方法.
- 虽然NLARX可以捕捉非线性模式,但ARMAX为这些数据提供了更好的解释性和预测稳定性.
- 研究结果支持使用多变量时间序列模型来基于证据的职业安全政策制定.
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