## 3 Results
### Statistical Analysis
#### 3.2.3 Evaluation of the Models Used  
As shown in Table 3, one of the most notable characteristics of the selected articles had to do with the variety of models used. A brief description of these models can be found in Table 1 of [88]. The number of models used exceeded the number of articles selected. This was due to the fact that 18 articles, corresponding to 41.86%, made comparative studies between various models, whereas the remaining articles used a single model. Within the latter group, MLP was the most used model, being considered in five articles, followed by CNN used in four studies, and SVM and RF, utilized in three articles each. For the complete set of selected studies, Figure 3A shows a bar chart with the models used and the number of times they appeared in the articles, differentiating those models that used symbolic processing (in red) and subsymbolic processing (in blue). It was taken into account that J48 is a model based on DT, VGG-16 is a model based on CNNs, and that SGD, AdaBoost, bagging, and vote method are training and optimization methods and therefore do not belong to the group of models used. Moreover, SL was incorporated into the group of LR. The ANN group contains both the unspecified ANN (since the corresponding articles suggested they were MLP networks) as well as BP-NN and FF-NN. As expected, since the dataset was made of time series, those models with subsymbolic processing predominated, being considered 82.61% of the time, whereas RF, DT, and RT were the only models utilized within the symbolic processing group. To emphasize these differences, it should be pointed out that whereas 17.39% of the articles considered models with symbolic processing, only two articles used them exclusively. Taking into account the previous groups, Figure 3A shows that the most used models in the articles included in this review were SVM and ANN, considered by 14 studies, followed by KNN, used by 10 studies, and RF and CNN models, which appeared in nine studies. The acronyms utilized were defined in the description in Table 3.  
The importance of Neural Machine Learning, which constituted 42.39% of the models used, although the most widely used models were SVM, a non-neural ML technique, and ANN in the same proportion, stand out. Deep Learning (DL) techniques are of special importance. Deep Neural networks are networks with more complex architectures that allow greater abstraction levels at the expense of increased computational power. So, the development of these models was expected to be concentrated in recent years. In fact, since 2019, we found 28 articles, of which we discarded [46; 54] for not providing enough information about the model used. Of the remaining 26 articles, 57.69% used Regarding the architecture of the models, it should be noted that, although it was specified for most of them, the degree of specificity of this information showed great variability among the articles. Actually, for the most complex models, parameters such as the number of units in each layer, the activation function used, the optimizer used, the learning rate value, or the loss function chosen, were specified in a few of the articles, and the associated information was not always uniform for all the models within every article. Moreover, the absence of a baseline complicated the comparison between the different studies. Both points made it difficult to provide a precise assessment of the architecture, and so of the Model Parameters column. However, it is worth emphasizing that it stood out the use of the Adam optimizer, the ReLU activation function, and Softmax for the last layer. Within the assessment of the training and validation phase, it could be observed that 24 of the selected articles (55.81%) used the K-fold cross-validation technique (two of which were LOO), and 16 of the selected articles considered a division in separate sets for training/validation/testing according to percentages fixed in each study.