## 3 Experimental Scenarios
### Scenario I: Experimental Results  
Regardless of the medium size of the training dataset, each submodel was trained for over 400 epochs to identify cases of overfitting. Since no early stopping mechanism was implemented for the slideNN model, training was deliberately extended to 400 epochs to evaluate its performance across a wide temporal range. During early experiments with fewer epochs (starting from 100), the training loss continued to decrease, suggesting room for further optimization. However, analysis of the training versus validation loss curves revealed that, after approximately 30-35 epochs, the validation loss began to increase while the training loss decreased, and it eventually surpassed it--a clear sign of overfitting. As indicative evidence, the training versus validation loss curve of a representative submodel (slideNN with input/output: \(64\to 2\)), as shown in Figure 6 below, clearly demonstrates this overfitting behavior. The remaining submodels (strands) exhibited similar patterns, further supporting this observation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configurations** & **Input/Output Size** & **Parameters (p)** & **Memory (KB)** \\ \hline
1 & 64/2 & 2790 \(\leq p\leq\) 3186 & 10.9 \(\leq KB\leq\) 12.45 \\
2 & 128/4 & 11,036 \(\leq p\leq\) 11,556 & 43.11 \(\leq KB\leq\) 45.14 \\
3 & 256/8 & 43,848 \(\leq p\leq\) 43,896 & 171.28 \(\leq KB\leq\) 171.47 \\
4 & 512/16 & 175,088 \(\leq p\leq\) 177,872 & 683.94 \(\leq KB\leq\) 694.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Model configurations with the parameter count and corresponding memory size. The resulting predictions were not highly accurate in absolute terms. However, the experiments did reveal a consistent, inductive pattern of improvement across the submodels. Specifically, as the output size increased from 2 to 16, each subsequent configuration produced better results than the previous one. The slideNN model performance improved inductively as more output steps were introduced, likely due to its capacity to capture broader temporal dependencies when given more extensive target horizons. To showcase the performance of each submodel within the slideNN architecture, Table 6 presents the loss (RMSE) and the respective MSE for each configuration. The slideNN model architecture, however, showed moderate improvements (\(d\) = 0.62-1.45), with all model strands achieving statistical significance (\(p<0.05\)). The \(512\to 16\) submodel demonstrated the strongest effect (\(d=1.45,p=0.025\)), though its RMSE (0.596) values were still extremely high. Furthermore, the slideNN models were faster than GRU in terms of training, inference, edge device implementation, and memory residence occupation. Therefore, slideNN was found to be the model that can infer results, even in 8 bit microcontroller units (MCUs), with the least device requirements. Following experimentation on the slideNN architecture, we conducted a corresponding series of evaluations to capture the temporal patterns using a GRU-based Recurrent Neural Network model. GRU was selected as it better at capturing, without suffering from the vanishing gradient problem, long-range dependencies than RNNs. It maintains fewer gates than LSTMs (two instead of three and as cell states), resulting in faster inferences and reduced memory usage. Four distinct input-output configurations were employed--64-2, 128-4, 256-8 (Figure 3), and 512-16--ensuring direct comparability between the two architectures. In addition to the input and output window sizes, the GRU model introduced two more key hyperparam