使用针对COVID-19优化的EVDHM-ARIMA进行非静止时间序列预测
Suraj Singh Nagvanshi1, Inderjeet Kaur1, Charu Agarwal1
1Department of Computer Science & Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, Uttar Pradesh, India.
Frontiers in big data
|June 30, 2023
概括
这项研究引入了一种新的时间序列预测模型,以预测COVID-19 (冠状病毒疾病2019) 的传播. 该模型结合了汉克尔矩阵 (EVDHM) 的优化 EigenValue 分解和自回归集成移动平均值 (ARIMA) 以准确预测感染率.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 由高度可变的SARS-CoV-2病毒引起的COVID-19大流行导致了广泛的感染和死亡.
- 新变种的出现,如COVID-19变种XE,需要强大的方法来追踪传播和预测未来的病例.
- 准确的预测对于公共卫生准备,资源分配和减轻死亡率至关重要.
研究的目的:
- 为非静止数据开发和验证一种新的时间序列预测模型,专门应用于COVID-19感染率.
- 通过将汉克尔矩阵 (EVDHM) 的优化 EigenValue 分解与优化自回归集成移动平均数 (ARIMA) 模型集成来提高预测准确性.
- 利用遗传算法 (GA) 来优化模型参数并改善时间序列组件的分解.
主要方法:
- 开发了一种混合预测模型,将EVDHM用于时间序列分解和ARIMA用于组件智能预测.
- 菲利普斯·佩伦测试 (PPT) 用于识别和确认时间序列数据中的非静止性.
- 基因算法 (GA) 用于优化ARIMA参数 (最小化AIC) 和EVDHM分解 (最小化非静止性,最大化自值利用).
主要成果:
- 开发的模型成功地使用EVDHM将非静止时间序列分解为多个组件.
- 每个分解组件都使用优化的ARIMA模型进行预测,通过GA选择参数以获得最佳的AIC.
- 从单个组件的综合预测提供了未来COVID-19病例和传播率的全面预测.
结论:
- 拟议的EVDHM-ARIMA混合模型为预测非静止时间序列提供了一种有效的方法,特别适用于COVID-19等传染病动态.
- 使用遗传算法的优化显著提高了模型的分解和预测组件的性能.
- 这种预测方法可以帮助公共卫生官员及时做出决定,以控制病毒的传播和减少死亡率.
相关概念视频
01:25Statistical Methods for Analyzing Epidemiological Data
425
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
425
01:29Econometric Views (EViews)
175
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
175
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
01:18Steps in Outbreak Investigation
154
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
154
00:54Time-Series Graph
4.4K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.4K
01:14Parametric Survival Analysis: Weibull and Exponential Methods
488
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
488