贝叶斯预测局限的Poisson分布式时间序列的贝叶斯预测
Feng-Chi Liu1, Cathy W S Chen1, Cheng-Ying Ho1
1Department of Statistics, Feng Chia University, Taichung 40724, Taiwan.
Entropy (Basel, Switzerland)
|January 22, 2024
概括
这项研究引入了贝叶斯的零-一-膨胀边界波桑自动回归 (ZOBPAR) 模型,用于时间序列数据. ZOBPAR模型准确地预测了包括空气质量指数水平在内的边界顺序数据.
科学领域:
- 统计 统计 统计 统计
- 时间序列分析时间序列分析
- 计量经济学 计量经济学
背景情况:
- 在空气质量指数 (AQI),经济学和信用评级中常见的普通时间序列数据通常与集中在特定状态 (例如0和1) 的概率相结合.
- 现有的模型可能无法充分捕捉这些零-一-膨胀边界数据集的独特特征.
研究的目的:
- 开发和验证贝叶斯式零-一-膨胀边界波桑自动回归 (ZOBPAR) 模型,用于建模和预测边界顺序时间序列.
- 扩展ZOBPAR模型以纳入外源变量,以提高预测能力.
主要方法:
- 为ZOBPAR模型提出贝叶斯推理方法.
- 将外源变量纳入ZOBPAR框架.
- 模拟研究用于评估参数估计的准确性和稳定性.
主要成果:
- 模拟结果证实了准确的参数估计,随着样本大小的增加,后面的平均值趋于真值.
- 对台湾每日AQI水平的实证应用表明,拟议的方法具有有效的预测能力.
- ZOBPAR模型在预测Miaoli站的AQI方面表现特别强.
结论:
- 提出的贝叶斯ZOBPAR模型提供了一个强大的和准确的方法来建模和预测有界的顺序时间序列数据.
- 包含外源变量代表了贝叶斯推理对这些类型的模型的重大进步.
- 该方法的实际实用性通过成功的AQI预测来证明,突出其在环境监测和其他领域的潜力.
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