双季自回归模型与真实应用的贝叶斯完全分析
1Department of Statistics, Mathematics, and Insurance, Faculty of Commerce, Menoufia University, Menoufia, Egypt.
Journal of applied statistics
|June 12, 2024
概括
本研究引入了对双季节自回归 (DSAR) 模型的统一贝叶斯方法,增强了对电力负载预测的时间序列分析. 该方法使用Gibbs采样有效处理模型识别,估计和预测.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 时间序列分析时间序列分析
背景情况:
- 多倍双季节自回归 (DSAR) 模型对于分析具有多个季节模式的复杂时间序列数据至关重要.
- 对于DSAR模型的现有方法往往缺乏统一的识别,估计和预测框架.
- 准确预测时间序列,例如电力负载,对于资源管理和经济规划至关重要.
研究的目的:
- 介绍一个全面的贝叶斯对乘法DSAR模型的分析.
- 为DSAR模型识别 (最佳子集选择),估计和预测制定统一的方法.
- 为季节性时间序列的多步预测提供一个强大的方法.
主要方法:
- 为DSAR模型提出了一个完整的贝叶斯分析框架.
- 对于模型滞后,引入了潜在变量,将DSAR模型嵌入到一个分层的贝叶斯正常混合结构中.
- 吉布斯采样用于近似后置和预测分布,使多步前置预测成为可能.
主要成果:
- 获得条件后面和预测分布的封闭形式导数.
- 拟议的吉布斯抽样算法有效地近似这些分布的DSAR模型.
- 该方法在模拟研究中的效率和对电力负载数据的实际应用得到了证明.
结论:
- 开发的贝叶斯方法为DSAR模型分析提供了一个统一而高效的框架.
- 该方法为季节性时间序列提供了准确的多步预测.
- 对欧洲电力负载数据的应用验证了拟议的贝叶斯式DSAR模型的实际实用性.
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