| Cheng et al. (2020), Ronghui and Liangrong (2021), Suneja et al. (2021) | RMSE | Root mean square error (RMSE) is the square root of the average of the squared differences between the estimated and the actual value of the variable/feature. RMSE is the standard deviation of the residuals, which are a measure of how far from the regression line data points are; RMSE is a measure of how spread out these residuals are. In other words, it measures how concentrated the data is around the line of best fit |
| Juszczyk and Leśniak (2019), Juszczyk et al. (2019), Juszczyk (2020), Cheng et al. (2020), Suneja et al. (2021) | MAPE | Mean absolute percentage error (MAPE) measures the accuracy as a percentage and can be calculated as the average absolute percentage error for each time period minus actual values divided by actual values |
| Cheng et al. (2020), Suneja et al. (2021) | MAE | Mean absolute error (MAE) is a measure of errors between paired observations expressing the same phenomenon. Examples of Y versus X include comparisons of predicted versus observed, subsequent time versus initial time and one technique of measurement versus an alternative technique of measurement |
| Cheng et al. (2020) | R2 | R2 or coefficient of determination is a regression score function. The results span from 1 to −1 (1 being the best possible). R2 measures the proportion of the change in the dependent variable from the independent variables |