基于EVDHM-ARIMA的时间序列预测模型及其对COVID-19病例的应用
Rishi Raj Sharma1, Mohit Kumar2, Shishir Maheshwari3
1Department of Electronics EngineeringDefence Institute of Advanced Technology Pune 411025 India.
IEEE transactions on instrumentation and measurement
|November 20, 2023
概括
本研究引入了汉克尔矩阵 (EVDHM) 和自回归集成移动平均线 (ARIMA) 模型的新型自身值分解,用于准确的非静止时间序列预测. 该方法有效预测印度,美国和巴西的COVID-19病例.
科学领域:
- 时间序列分析分析时间序列分析
- 统计建模 统计建模
- 流行病学预测 流行病学预测
背景情况:
- 准确的时间序列预测对于有效的决策至关重要.
- 非静止时间序列对传统预测模型构成重大挑战.
- 现有的方法可能难以应对现实数据的复杂性和动态性质.
研究的目的:
- 为非静止时间序列开发一个强大的预测模型.
- 通过解决数据非静止性来提高预测准确性.
- 应用该模型预测COVID-19新日病例.
主要方法:
- 使用汉克尔矩阵的自值分解 (EVDHM) 来分解和减少时间序列中的非静态性.
- 雇佣的自回归集成移动平均 (ARIMA) 模型用于预测子组件.
- 使用遗传算法 (GA) 优化了ARIMA参数,以最大限度地减少Akaike信息标准 (AIC).
- 应用了菲利普斯-佩伦测试 (PPT) 来确定时间序列的非静止性.
主要成果:
- 在预测方面,EVDHM-ARIMA模型表现出了很高的效率.
- 成功将非静止时间序列分解为可管理的子组件.
- 对印度,美国和巴西的每日新增COVID-19病例的准确预测得到了实现.
- 遗传算法有效地优化了ARIMA参数,以提高准确性.
结论:
- 提出的EVDHM-ARIMA方法为非静止时间序列预测提供了一个强大的方法.
- 这种技术为流行病监测等关键应用提供了可靠的预测.
- 这项研究验证了模型在真实世界的流行病数据上的有效性.
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