➕Dataset and Parameter Optimization

70% of the dataset is used as the training set, while the remaining 30% is reserved as the testing set and is not involved in the model training process. Furthermore, within the testing dataset, we have also allocated 30% of the training data as a validation split while training the model, to help the model adjust its weights in a more generalized manner and avoid overfitting during training. The model is trained with the objective of minimizing the Mean Absolute Error as the central metric. The model is trained iteratively by varying different parameters within the model. Learning rate, activation function, number of CNN layers, number of filters, number of LSTM layers, and number of neurons in each layer have been experimented with to optimize the model architecture (Figure 5). The bold values indicate the values used that are considered optimal for a specific model. It is worth noting that changing the number of CNN layers in the model slightly improved accuracy, but not significantly enough to justify the additional computational cost. Similarly, with the number of filters. The parameter optimization settings can be seen in Table 2, and the training and testing error metrics can be observed in Tables 3 and 4, respectively.

Table 2. Parameter optimization value setting

Parameters	Values
Learning rate	0.0001, 0.0005, 0.001, 0.005, 0.1
Activation function	ReLu, Tanh
Activation function	1,2,3,4
CNN Layers	1,2,3
No. neurons in first layer of LSTM and TDL	128,256,512
No. filters	1,2,4,8…32

Table 3. Training error metrics

Metric	Values
Train R-Square	0.985
Train Mean Absolute Error (MAE)	167.558
Train Mean Absolute Percentage Error (MAPE)	0.0224
Train root mean square error (RMSE)	199.036

Table 4. Testing error metrics

Metric	Values
Train R-Square	0.941
Train Mean Absolute Error (MAE)	242.408
Train Mean Absolute Percentage Error (MAPE)	0.031
Train root mean square error (RMSE)	413.912