| dc.contributor.author | Hongxu Zhu | |
| dc.contributor.author | Petrov, Dmitriy | |
| dc.contributor.author | Razumeichik, Vita | |
| dc.coverage.spatial | Брест | |
| dc.date.accessioned | 2023-01-23T07:06:48Z | |
| dc.date.available | 2023-01-23T07:06:48Z | |
| dc.date.issued | 2022 | |
| dc.identifier.citation | Zhu Hongxu A comparison of the Covid-19 machine learning automation model and SPSS time series / Hongxu Zhu, D. O. Petrov, V. S. Razumeichik // Цифровая среда: технологии и перспективы. DETP 2022, Брест, 31 октября 2022 г. / Министерство образования Республики Беларусь, Брестский государственный технический университет ; редкол.: Н. Н. Шалобыта [и др.]. – Брест : БрГТУ, 2022. – С. 65–70. | |
| dc.identifier.uri | https://rep.bstu.by/handle/data/32461 | |
| dc.description.abstract | This paper uses publicly available data on the prediction process of Covid-19 transmission in the world to attempt to predict the time series using the SPSS exponential Holt model and the Python ARIMA model. model model to predict the epidemic development trend and key nodes, quantitative analysis of the scale of the epidemic, scientific and reliable interval estimation of the original base and effective transmission rate of the epidemic and comparative analysis of different algorithms, providing an effective basis and guide for analysis, command and decision making in the prevention and control of the epidemic. | |
| dc.language.iso | en | |
| dc.publisher | БрГТУ | |
| dc.title | A comparison of the Covid-19 machine learning automation model and SPSS time series | |
| dc.type | Научный доклад (Working Paper) | |
| dc.identifier.udc | 004.942 | |
