论文标题
通过动力学系统的多个轨迹进行多元时间序列序列近似。应用于Internet流量和COVID-19数据的应用程序
Multivariate time series approximation by multiple trajectories of a dynamical system. Applications to Internet traffic and COVID-19 data
论文作者
论文摘要
动态系统模型的多个轨迹的利用为我们提供了近似时间序列的几个好处。对于短期预测，可以随时通过切换到新轨迹来实现高精度。相位肖像的不同长期趋势（趋势趋于固定点的趋势）表征了受外部性影响的过程实现的各种情况。动力学系统的相位肖像分析有助于查看方程是否正确描述了现实。我们还将动态系统方法（在\ cite {r5}中讨论）扩展到具有外部控制的动态系统。 我们借助租赁物业房屋的新示例来说明这些想法。我们还比较了房屋的定性属性。mil和wikipedia.org平台的相位肖像以及两个平台用户的相应差异。在COVID-19数据的最后一个示例中，我们讨论了确认的感染案件，恢复病例和死亡病例的短期预测的高度准确性。
Utilization of multiple trajectories of a dynamical system model provides us with several benefits in approximation of time series. For short term predictions a high accuracy can be achieved via switches to new trajectory at any time. Different long term trends (tendency to different stationary points) of the phase portrait characterize various scenarios of the process realization influenced by externalities. The dynamical system's phase portrait analysis helps to see if the equations properly describe the reality. We also extend the dynamical systems approach (discussed in \cite{R5}) to the dynamical systems with external control. We illustrate these ideas with the help of new examples of the rental properties HOMES.mil platform data. We also compare the qualitative properties of HOMES.mil and Wikipedia.org platforms' phase portraits and the corresponding differences of the two platforms' users. In our last example with COVID-19 data we discuss the high accuracy of the short term prediction of confirmed infection cases, recovery cases and death cases in various countries.
