Table 4 Comparison of BeCaked and top-tier forecasting-specialized methods at country level with 1-step forecast.
From: BeCaked: An Explainable Artificial Intelligence Model for COVID-19 Forecasting
BeCaked | ARIMA | Ridge | LASSO | SVR | DTR | RFR | GBR | |
|---|---|---|---|---|---|---|---|---|
Australia | ||||||||
\(R^2\) | 0.99112 | 0.98329 | 0.99337 | \(\mathbf {0.99531}\) | \(-0.48798\) | 0.98342 | 0.96787 | 0.98299 |
MAPE | 0.04703 | 0.10236 | \(\mathbf {0.01093}\) | 0, 04078 | 0.85609 | 0.10204 | 0.14942 | 0.10344 |
Italy | ||||||||
\(R^2\) | \(\mathbf {0.96381}\) | 0.94660 | 0.92646 | 0.88220 | \(-22.56497\) | 0.94889 | 0.90082 | 0.94876 |
MAPE | 0.00496 | 0.01655 | \(\mathbf {0.00024}\) | 0.05999 | 0.93214 | 0.01684 | 0.02570 | 0.02165 |
Russia | ||||||||
\(R^2\) | 0.92922 | 0.91663 | \(\mathbf {0.98479}\) | 0.92367 | \(-108.89720\) | 0.91643 | 0.87353 | 0.91854 |
MAPE | \(\mathbf {0.00004}\) | 0.02856 | 0, 00007 | 0.00938 | 0.77448 | 0.02851 | 0.04442 | 0.02938 |
Spain | ||||||||
\(R^2\) | \(\mathbf {0.98765}\) | 0.97214 | 0.98591 | 0.98362 | \(-10.84390\) | 0.97212 | 0.94968 | 0.97053 |
MAPE | \(\mathbf {0.00174}\) | 0.01418 | 0.00325 | 0.01881 | 0.74045 | 0.01406 | 0.02173 | 0.01522 |
United Kingdom | ||||||||
\(R^2\) | \(\mathbf {0.99833}\) | 0.98713 | 0.99764 | 0.96375 | \(-449.89408\) | 0.98748 | 0.96991 | 0.98463 |
MAPE | 0.00030 | 0.00255 | \(\mathbf {0.00014}\) | 0.00437 | 0.52586 | 0.00249 | 0.00397 | 0.00282 |
United States | ||||||||
\(R^2\) | \(\mathbf {0.99688}\) | 0.98676 | 0.99648 | 0.99461 | \(-9.99942\) | 0.98676 | 0.96834 | 0.98633 |
MAPE | 0.00552 | 0.02644 | \(\mathbf {0.00004}\) | 0.00069 | 0.62264 | 0.02642 | 0.04185 | 0.02687 |