## 3 Experimental Scenarios
### Scenario I: Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Submodel** & **RMSE** & **MSE** & \(p\)**-Value** & **Effect Size** \\ (**Input**\(\rightarrow\)**Output**) & **(Mean**\(\pm\)**\(\sigma\)**)** & **(Mean**\(\pm\)**\(\sigma\)**)** & **(vs. 64**\(\rightarrow\)**2**)** & **(Cohen’s d)** \\ \hline
64 \(\rightarrow\) 2 & 0.786 \(\pm\) 0.051 & 0.617 \(\pm\) 0.048 & – & – \\
128 \(\rightarrow\) 4 & 0.674 \(\pm\) 0.039 & 0.454 \(\pm\) 0.035 & 0.035 & 0.62 \\
256 \(\rightarrow\) 8 & 0.641 \(\pm\) 0.032 & 0.410 \(\pm\) 0.028 & 0.028 & 0.85 \\
512 \(\rightarrow\) 16 & 0.596 \(\pm\) 0.025 & 0.355 \(\pm\) 0.021 & 0.025 & 1.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Conservative performance measures for the slideNN model (mean \(\pm\) std over 10 runs). Figure 6: The training vs. validation loss curve of the slideNN submodel 1: 64 \(\rightarrow\) 2, indicating signs of overfitting from the epoch 30–35 and onward. eters: the number of GRU cells and the number of internal layers. For each configuration, the number of cells was carefully selected so that the total number of trainable parameters closely matched that of its slideNN counterpart. The selected values were 16, 32, 64, and 128 cells for the respective input-output pairs, which can run on 32-bit microprocessor devices with at least 1-2 MB of available RAM. Regarding the internal GRU layers, in the context of the resource-constrained edge computing scenario, this hyperparameter was set to a constant value of two layers across all configurations. This design choice was also driven by the need to maintain a parameter count comparable to that of the corresponding slideNN models, ensuring a fair and consistent basis for comparison. Training was performed over 50 epochs with a batch size of 32. In the GRU model, both in the edge and cloud (hybrid model) cases (see Scenario II, Section 3.3), using an early stopping function with patience of 3 epochs of no RMSE change being present (\(\leq\)10\({}^{-3}\)) helped to select the most appropriate training epochs for the most desirable results. Compared to slideNN, the GRU architecture required significantly fewer epochs to converge, largely due to its recurrent structure, which is inherently more capable of capturing the temporal dependencies in sequential data. The problem of overfitting did not occur in this case, and the corresponding training versus validation loss curve graph, as shown in Figure 7 below, further confirmed this finding. In this instance, the model with an input/output configuration of 256 \(\rightarrow\) 8 was chosen because it showed a significant improvement over its slideNN counterpart strands and demonstrated practical utility, despite being one of the larger models in an edge environment. Maintaining the same parameter settings across both sub-scenarios, it became evident that the GRU-based architecture consistently achieved better results compared to the slideNN model, regardless of the input-output configuration. A clear inductive improvement was still observed in the prediction performance as both the number of timesteps and GRU cells increased. This trend was reflected in the gradual reduction in errors. These findings indicate that the GRU architecture benefits substantially from increased complexity, improving its ability to capture long-term dependencies and patterns within the data. The performance results of each configuration of the GRU architecture for edge computing are shown in Table 7.  
Figure 7: The training vs. validation loss curve of the GRU Model 3: 256 \(\rightarrow\) 8, showing the smooth and linear training and validation loss curves throughout all epochs. As shown in Table 7, all the configurations remained statistically significant (\(p<0.05\)) with effect sizes constrained to \(d<2.0\). The \(128\to 4\) variant showed a moderate effect of d = 0.85, while larger models achieved \(d>1.65\), confirming their practical utility despite edge hardware limitations. The \(512\to 16\) model retained the strongest improvement (RMSE = 0.233, d = 1.90, \(p\) = 0.020).