论文标题
COVID-19来自软数据多变量曲线回归和机器学习的死亡率分析
COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning
论文作者
论文摘要
介绍了多个客观时空预测方法，涉及周期性曲线对数回归和多元时间序列空间残留相关分析。具体而言，在三角回归的框架中，平均二次损耗函数被最小化。而在随后的空间残差相关分析中，可能性的最大化使我们能够在贝叶斯多元时间序列软数据框架中计算后验模式。自2020年3月8日，至2020年5月13日至2020年5月13日以来，提出的方法应用于影响西班牙社区的第一波互联-19死亡率的分析。基于随机K折叠式验证，对机器学习（ML）回归进行了经验比较研究，并进行了自动化置信置信度的置信区间和概率估计。该经验分析还研究了在硬数据框架和软数据框架中ML回归模型的性能。结果可以推断到其他计数，国家和后Covid-19波。
A multiple objective space-time forecasting approach is presented involving cyclical curve log-regression, and multivariate time series spatial residual correlation analysis. Specifically, the mean quadratic loss function is minimized in the framework of trigonometric regression. While, in our subsequent spatial residual correlation analysis, maximization of the likelihood allows us to compute the posterior mode in a Bayesian multivariate time series soft-data framework. The presented approach is applied to the analysis of COVID-19 mortality in the first wave affecting the Spanish Communities, since March, 8, 2020 until May, 13, 2020. An empirical comparative study with Machine Learning (ML) regression, based on random k-fold cross-validation, and bootstrapping confidence interval and probability density estimation, is carried out. This empirical analysis also investigates the performance of ML regression models in a hard- and soft- data frameworks. The results could be extrapolated to other counts, countries, and posterior COVID-19 waves.
