# Order-Based Schedule of Dynamic Topology for Recurrent Neural Network
## 5 Discussion  
Through experimentation, certain variations of the method of the proposed algorithm were designed, such as the extension of the range of the windows for the calculation of the order, given that, initially, when only the new data was analyzed, our algorithm determined to make abrupt changes to the network topology since the system had a lack of context of the previous data. When it was determined the need to expand the amount of data, adding the previous data window to the current one, an improvement in order detection was obtained. Another section that presented modifications in the experimentation process was the generation of the decision-making rules of the tree as its hierarchy. Looking to create a dependency between the decisions made over time and the evolution of the error metrics, the previous decision was assigned in the first position of the decision-making hierarchy followed by the error variation, thus presenting a distribution of rules that decreased or maintained the trend of the error. It is important to note that in almost all of the 14 tests, our approach improved the results obtained by the vanilla LSTM models; in the cases that didn't improve, the models keep almost the same performance as their counterparts. These cases were presented in five series (3, 4, 5, 11, 14) in just 7 models, where the worst model was presented in series 3, as can be seen in Table 7. During the experiments, in general, these results happened because in one of the phases, almost always after when a considerable change of order happened and a modification on the topology was made, the dynamic model had an error worse than the vanilla model, being this error enough to make on average that the dynamic model be worse in the whole test. As an example, Figure 8 and Tables 8 and 9 is presented one of the tests of series 11, which had a major number of models with no improvement, where can be seen that models 1 and 4 had an error during phase 2 that avoided them to be on average, at the end, the same or better accuracy than the vanilla models, but getting a topology smaller than the original. Despite this fact, the models usually reduce their error by getting the same or better values than their counterparts during the next phases. Figure 8: Graph of the MSE metric (Solid and Dotted Lines, measured by left axis), Order evolution (CAO), and the decisions made by the algorithm (TR-Bars, the last two metrics measured by the right axis) in test eleven. Other particularity presented in the experiments can be seen in tests 5 and 9, where the first phases, didn't present changes of order as can be seen in Table 6, when this happens, the models can present the issue to be the same as their vanilla version, that's why this issue was addressed using the normal phase training, giving the model the capability to manage the old knowledge and be able to distribute the new information over the whole architecture avoiding changing their topology. This implementation gave an improve over 40% in test 5 on models 2, 3, 4, and 5. Even though the dynamic model 1 didn't present an accuracy improvement w.r.t Vanilla, it stays near the same performance as its counterpart. One of these results can be seen in Figures 9 and 10 and Table 10, where it is presented the evolution of the MSE error and the rate of the architecture. Exemplifying the point described above, where architecture modifications are only seen in the last phases.