## 5 Experiments
### Parameter Selection Experiments and Results  
In the parameter selection experiment, we aim to determine the optimal parameter settings for the TransTLA model to optimize performance on home appliance sales forecasting. We focus on the impact of weight parameters in the composite loss function and the effect of the varying historical window sizes. The composite loss function (see Equation (20)) includes source domain loss, target domain loss, and domain adaptation loss, with hyperparameters \(\alpha\) and \(\beta\) balancing their contributions. \(\alpha\) is a critical hyperparameter that balances the weights between the source domain regression loss and the target domain regression loss. We explore \(\alpha\) values from 0 to 1 with steps of 0.1 to determine its effect. Specifically, when \(\alpha\) equals 0, the influence of the source domain regression loss is not considered; only the target domain regression loss and domain adaptation loss are used in the backpropagation. When \(\alpha\) is set to 1, it indicates that the total loss consists entirely of the source domain regression loss and domain adaptation \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Setting Value** \\ \hline Iterations & 150 \\ Batch Size & 32 \\ TCN Channels & 64 \\ Kernel Size & 3 \\ Kernel Stride & 1 \\ Kernel Padding & 1 \\ Residual Blocks & 2 \\ Dilation Factor & 1, 2 \\ LSTM Layers & 2 \\ Hidden Layer Size & 64 \\ Initial Learning Rate & 0.001 \\ Learning Rate Growth & 0.0003 \\ Learning Rate Decay & 0.75 \\ Dropout Rate & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model parameter settings in the experiment. loss. For each value of \(\alpha\), the model is trained with a consistent structure and parameters as described in Section 5.3. The historical backtracking window length is set to 7 days, and \(\beta\) is set to 0.1. We use a sliding window to predict the sales volume for the next day. The evaluation metrics for sales volume prediction under different proportions of source domain regression loss are presented in Table 3, with comparative curves for the MAE and RMSE indicators illustrated in Figure 8.  
Both Table 3 and Figure 8 indicate that the model performs well when the value of \(\alpha\) is in the middle range, specifically at 0.5 or 0.6, with a small difference between them. As the value of \(\alpha\) moves away from the middle, the predictive performance declines, suggesting that equally considering the regression losses of both the source and target domains leads to a more balanced effect, which helps the model maintain a better performance when transferred to the target domain. Therefore, 0.5 is chosen as the optimal parameter setting for \(\alpha\). It can be observed that, when \(\alpha=0\) or \(\alpha=1\), the model's performance is not ideal, indicating that relying solely on target domain data or source domain data is not the best strategy. A moderate amount of source domain information helps improve the predictive performance in the target domain task, but overreliance on source domain information may introduce noise or inadaptability, thereby reducing the model's performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\alpha\) & \(\mathbf{R^{2}}\) & MAE & RMSE & MAPE \\ \hline
0 & 0.483 & 17.677 & 27.229 & 0.438 \\
0.1 & 0.816 & 10.804 & 16.361 & 0.229 \\
0.2 & 0.815 & 10.415 & 16.389 & 0.210 \\
0.3 & 0.812 & 10.327 & 16.511 & 0.212 \\
0.4 & 0.826 & 9.721 & 15.923 & 0.180 \\
0.5 & 0.844 & **9.295** & 15.048 & **0.171** \\
0.6 & **0.845** & 9.786 & **15.024** & 0.179 \\
0.7 & 0.816 & 10.657 & 16.349 & 0.229 \\
0.8 & 0.800 & 10.736 & 17.018 & 0.225 \\
0.9 & 0.802 & 10.742 & 16.920 & 0.233 \\
1 & 0.766 & 12.128 & 18.123 & 0.279 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results under different ratios of source domain regression loss. Figure 8: Comparison curves of the evaluation metrics under different ratios of source domain regression loss. Next, we explore the impact of the hyperparameter \(\beta\) on the performance of the TransTLA model.