Research Article
Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models
Table 14
MAE values of support vector regression models for fatal cases.
| Forecast period (days) | Cumulative cases | Daily cases | Germany | Japan | South Korea | Ukraine | Germany | Japan | South Korea | Ukraine |
| Train 3 | 399.16 | 90.81 | 72.14 | 617.96 | 50.06 | 14.44 | 28.87 | 40.12 | Test 3 | 102.69 | 180.45 | 20.51 | 64.21 | 41.55 | 21.76 | 5.51 | 21.52 | Train 7 | 681.17 | 171.72 | 214.52 | 1067.67 | 50.84 | 15.75 | 30.78 | 44.9 | Test 7 | 225.59 | 461.32 | 21.5 | 38.32 | 27.67 | 14.07 | 3.79 | 15.48 | Train 14 | 1202.87 | 321.98 | 641.75 | 1778.12 | 60.34 | 19.37 | 33.74 | 58.43 | Test 14 | 352.16 | 1085.13 | 22.18 | 136.92 | 24.66 | 36.1 | 9.06 | 24.67 | Train 21 | 1723.38 | 476.29 | 1037.56 | 2673.69 | 68.13 | 24.05 | 39.41 | 67.3 | Test 21 | 439.12 | 1787.47 | 20.5 | 394.72 | 22.38 | 39.28 | 11.32 | 42.76 | Train 30 | 2471.49 | 743.6 | 1612.14 | 3814.64 | 77.89 | 27.76 | 41.99 | 89.43 | Test 30 | 496.3 | 2963.69 | 19.63 | 985.68 | 22.33 | 37.67 | 13.84 | 57.79 |
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