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APPLICATION OF NEURAL NETWORK TECHNOLOGIES IN PREDICTION OF COVID-19 INFECTION IN THE WORLD
Version 1
: Received: 28 March 2021 / Approved: 30 March 2021 / Online: 30 March 2021 (14:16:28 CEST)
How to cite: Dadyan, E. APPLICATION OF NEURAL NETWORK TECHNOLOGIES IN PREDICTION OF COVID-19 INFECTION IN THE WORLD. Preprints 2021, 2021030746 Dadyan, E. APPLICATION OF NEURAL NETWORK TECHNOLOGIES IN PREDICTION OF COVID-19 INFECTION IN THE WORLD. Preprints 2021, 2021030746
Abstract
For analysis tasks, time counts are of interest — values recorded at some, usually equidistant, points in time. The calculation can be performed at various intervals: after a minute, an hour, a day, a week, a month, or a year, depending on how much detail the process should be analyzed. In time series analysis problems, we deal with discrete-time, when each observation of a parameter forms a time frame. The same can be said about the behavior of Covid-19 over time.In this paper, we solve the problem of predicting Covid-19 diseases in the world using neural networks. This approach is useful when it is necessary to overcome difficulties related to non-stationarity, incompleteness, unknown distribution of data, or when statistical methods are not completely satisfactory. The problem of forecasting is solved with the help of the analytical platform Deductor Studio, developed by specialists of the company Intersoft Lab of the Russian Federation. When solving this problem, appropriate methods were used to clean the data from noise and anomalies, which ensured the quality of building a predictive model and obtaining forecast values for tens of days ahead. The principle of time series forecasting was also demonstrated: import, seasonal detection, cleaning, smoothing, building a predictive model, and predicting Covid-19 diseases in the world using neural technologies for 30 days ahead.
Keywords
time series; forecasting; neural networks; data preprocessing; training and control samples; coronavirus pandemics; Deductor Studio; data cleaning; partial processing; spectral processing; autocorrelation; sliding windows.
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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