## 6 Conclusions  
In this paper, we have proposed a novel TL approach with TCN-LSTM-Attention for household appliance sales forecasting in small towns, addressing the challenge of limited data availability. First, we cascaded the TCN and LSTM networks to capture the spatiotemporal relationships within historical sales data. Subsequently, based on these extracted features from the sales data, we utilized the attention mechanism to assign different weights by calculating the relevance between query, key, and value, dynamically focusing on different points in the sequence and further enhancing the model's focus on key information. Finally, by introducing a redefined composite loss function, we performed the TL technique between the source domain and target domain to address the challenges posed by limited data availability and regional variations. By transferring the knowledge gained from modeling sales data in large cities, we bolstered the predictive ability of small towns. The experimental results show that the proposed TransTLA approach outperforms traditional or combined methods such as RNN, LSTM, and CNN-LSTM-Attention in the context of small town household appliance sales forecasting.  
The proposed methodology has potential applications beyond household appliances, extending to other product categories such as electronics, automotive, and furniture. Accurate sales forecasting can help businesses optimize inventory management, improve supply chain efficiency, and enhance customer satisfaction. For governments, such forecasts can assist in economic planning, policy making, and resource allocation, thereby contributing to economic stability and growth.  
However, the current study has some limitations. The model's reliance on historical data presupposes the continuation of past patterns, which may not always be the case. Future research will investigate the adaptability of this model to diverse product categories and various market conditions, thereby broadening its applicability. Additionally, we  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **FLOPs (M)** & **MACs** & **Params (k)** \\ \hline TCN & 25.4536 & 12.7191 M & 51.906 \\ LSTM & 22.5556 & 66.56 k & 52.61 \\ TCN-LSTM & 54.314 & 12.3259 M & 106.178 \\ TCN-Attention & 27.0267 & 13.5055 M & 51.969 \\ LSTM-Attention & 24.9152 & 1.2462 M & 65.026 \\ TCN-LSTM-Attention & 56.6735 & 13.5055 M & 118.594 \\ TransTCN & 50.9071 & 25.4382 M & 51.906 \\ TransLSTM & 45.1113 & 133.12 k & 52.61 \\ TransTCN-LSTM & 108.628 & 24.6518 M & 106.178 \\ TransTCN-Attention & 54.0533 & 27.0111 M & 51.969 \\ TransLSTM-Attention & 49.8303 & 2.4924 M & 65.026 \\ RNN & 5.6965 & 66.56 k & 14.786 \\ CNN & 21.3268 & 10.5083 M & 399.681 \\ CNN-LSTM & 284.126 & 6.3795 M & 75.074 \\ CNN-LSTM-Attention & 301.43 & 15.0303 M & 87.49 \\ TransCNN-LSTM-Attention & 602.86 & 30.0605 M & 87.49 \\
**TransTLA** & 113.347 & 27.0111 M & 118.594 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Computational complexity of various models.  
intend to examine the sensitivity of the TL component in TransTLA to domain variations and explore strategies to mitigate the potential presence of negative transfer, particularly in scenarios where the source and target domains differ significantly, further improving the robustness and effectiveness of our approach.  
Conceptualization, Z.H.; methodology, Z.H.; software, Z.H.; validation, Z.H.; formal analysis, Z.H.; investigation, Z.H. and J.L.; writing--original draft preparation, Z.H.; writing--review and editing, Z.H. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy.  
The authors declare no conflicts of interest.