*5.4. Ensemble Results*

Ensemble models of the three techniques were developed using the aggregate method with two individual developed models at a time; that is, from Equation (16), *n* = 2. All the individual models developed in this research were ensembled in this manner and their performance was observed. The performance parameters for the ensemble model whose year-ahead UCLF forecast achieved the lowest errors per experiment are presented in Table 7. Thus, not all results are included in Table 7, just the results with the lowest errors per experiment. The ensemble technique name is constructed by combining the name of the original technique used and the number of hidden nodes, for the OP-ELM and DBN, and the number of hidden units, for the LSTM, next to the name. The lowest obtained errors were achieved using an ensemble model of two LSTM models with 192 and 26 hidden units, respectively. This model achieved an sMAPE of 6.43%, MAE of 7.36%, and RMSE of 9.21%, which are bolded in Table 7. The respective errors in Experiments 4 and 5 were approximately twice the errors in Experiment 1. The accuracy of the model in Experiment 2 was higher than that for the models in Experiment 3. The models in Experiments 2 and 3 had lower accuracy than the model in Experiment 1, and higher accuracy than the models in Experiments 4 and 5.

**Table 7.** Ensemble experiments results.



**Table 7.** *Cont*.

Table 8 presents the results for a statistical significance test conducted as discussed in Section 3.5. A *p*-value less than 0.05 was observed for each test conducted. This observation indicated that all the results being compared were significantly different from each other.

**Table 8.** Ensemble models' lowest errors statistical significance test.

