## 4 Discussion  
From the author's experimentation regarding the GRU cloud and edge models, as expected, the cloud-based GRU models demonstrated consistent superiority over the edge implementations, with RMSE improvements scaling from 21.9% (64 \(\to\) 2) to 34.1% (128 \(\to\) 4) (Tables 7-9). This performance gap narrowed at larger configurations (13.7% for 512 \(\to\) 16), suggesting diminishing returns from cloud resources for long-sequence predictions. Notably, the cloud 256 \(\to\) 8 model achieved a 32.8% lower RMSE (0.213 vs. 0.317) with 2.8 times more parameters (37.2M vs. 43.9K), highlighting the cloud's ability to leverage increased capacity for mid-range temporal patterns. While the cloud models required more than 100 MB of memory (see Table 8), tje edge GRUs maintained a small footprint of 700 KB (see Table 5), with a proportional accuracy loss. The 512 \(\to\) 16 edge configuration (RMSE = 0.233) achieved 80% of the cloud model's accuracy (RMSE = 0.201) using 0.5% of the parameters, demonstrating exceptional efficiency for resource-constrained deployments. This suggests edge GRUs are viable when cloud latency or costs are prohibitive, particularly for shorter prediction horizons. On the other hand, slideNN models trained on a small number of epochs are suitable for edge devices, such as ultra-small microcontroller units, providing fair forecasting accuracy results. Despite their larger size, the cloud models converged in 25-40 training epochs compared to the edge GRUs' 50 epochs and slideNN's 400 epochs. This accelerated convergence (Figure 8) stems from the cloud's ability to process larger batches (32 vs. edge's 16) and exploit parallelization. However, the edge models showed more stable training curves, with lower variance in the final RMSE (\(\pm\)0.011 vs. cloud's \(\pm\)0.015 for 512 \(\to\) 16), indicating better generalization under hardware constraints. The choice of batch sizes, specifically the batch values of 16 and 32, was primarily driven by established well-documented performance and use in deep learning applications. For the slideNN model, a batch size of 16 was selected primarily due to its better generalization capabilities, as the model demonstrated increased difficulty in retaining useful information. Furthermore, this choice was motivated by the reduced memory consumption since the model is intended for deployment on resource-constrained edge devices and to moderate overfitting issues observed during training, as discussed in the experimental results of scenario detailed in Section 3.2. In contrast, the GRU-based model exhibited greater stability during training, did not suffer from overfitting, and had higher memory requirements. Therefore, a batch size of 32 was deemed more appropriate for this model. A cross-scenario analysis revealed a clear trade-off: the cloud models reduced the root mean square error (RMSE) by 21-34% but required 150-200 times more memory and 3-5 times more energy per inference. For time-critical applications, such as real-time Air Quality Index (AQI) alerts, edge GRUs offer the best balance between energy consumption and accuracy. It is important to note that cloud models are the offline analysis AI tools used by latency-tolerant systems. This highlights the importance of developing architecture-specific deployment requirements and instructions tailored to specific use cases. Table 10 summarizes the aforementioned conclusions and contains the strongest models. Each model was evaluated based on different criteria, such as efficiency, accuracy, and resource availability. The models were compared with each other, offering insights into their distinct practical utility. The mixing of GRU and dense neural layers in the GRU-NN architecture (hybrid model) focuses on the complementary strengths of each previously modeled GRU and slideNN model component. On the one hand, the slideNN model (a plain feedforward network) demonstrated a moderate ability to generalize temporal dependencies, particularly as the output horizons increased.