Author: Sina F. Ardabili; Amir MOSAVI; Pedram Ghamisi; Filip Ferdinand; Annamaria R. Varkonyi-Koczy; Uwe Reuter; Timon Rabczuk; Peter M. Atkinson
Title: COVID-19 Outbreak Prediction with Machine Learning Document date: 2020_4_22
ID: nu0pn2q8_35
Snippet: Time-series are data sequences collected over a period of time [75] , which can be used as inputs to ML algorithms. This type of data reflects the changes that a phenomenon has undergone over time. Let X t be a time-series vector, in which xt is the outbreak at time point t and T is the set of all equidistant time points. To train ML methods effectively, we defined two scenarios, listed in Table 3 . As can be seen in Table 3 , scenario 1 employs .....
Document: Time-series are data sequences collected over a period of time [75] , which can be used as inputs to ML algorithms. This type of data reflects the changes that a phenomenon has undergone over time. Let X t be a time-series vector, in which xt is the outbreak at time point t and T is the set of all equidistant time points. To train ML methods effectively, we defined two scenarios, listed in Table 3 . As can be seen in Table 3 , scenario 1 employs data for three weeks to predict the outbreak on day t and scenario 2 employs outbreak data for five days to predict the outbreak for day t. Both of these scenarios were employed for fitting the ML methods. In the present research, two frequently used ML methods, the multi-layered perceptron (MLP) and adaptive network-based fuzzy inference system (ANFIS) are employed for the prediction of the outbreak in the five countries.
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