“…Traditional holdout randomly splits the dataset assuming that the frequency distributions do not change over time; temporal holdout divides the dataset taking into account the temporal evolution of the data. Except for two studies that do not mention the percentage of the dataset used in each partition [40], [2], all other studies used traditional holdout, varying the proportion of the dataset in the training and testing partitions, as follows: 80% -20% [27], [34], 75% -25% [35], 70% -30% [38], and 50% -50% [20], [3]. The k-fold method randomly divides the dataset into k unique parts of the same size, trains the forecast model with parts, and uses the remaining part for validation.…”