###### Abstract  
Deep learning (DL) has been widely applied to forecast the sales volume of household appliances with high accuracy. Unfortunately, in small towns, due to the limited amount of historical sales data, it is difficult to forecast household appliance sales accurately. To overcome the above-mentioned challenge, we propose a novel household appliance sales forecasting algorithm based on transfer learning, temporal convolutional network (TCN), long short-term memory (LSTM), and attention mechanism (called \"TransTLA\"). Firstly, we combine TCN and LSTM to exploit the spatiotemporal correlation of sales data. Secondly, we utilize the attention mechanism to make full use of the features of sales data. Finally, in order to mitigate the impact of data scarcity and regional differences, a transfer learning technique is used to improve the predictive performance in small towns, with the help of the learning experience from the megacity. The experimental outcomes reveal that the proposed TransTLA model significantly outperforms traditional forecasting methods in predicting small town household appliance sales volumes. Specifically, TransTLA achieves an average mean absolute error (MAE) improvement of 27.60% over LSTM, 9.23% over convolutional neural networks (CNN), and 11.00% over the CNN-LSTM-Attention model across one to four step-ahead predictions. This study addresses the data scarcity problem in small town sales forecasting, helping businesses improve inventory management, enhance customer satisfaction, and contribute to a more efficient supply chain, benefiting the overall economy. household appliance sales forecasting; transfer learning; temporal convolutional network; long short-term memory; attention mechanism +
Footnote †: journal: Applied  
In recent years, house appliances sales forecasting has been widely studied in [3; 4; 5]. It is noted that the widely used forecasting method can be classified into three types: (1) statistical probability models [6], (2) machine learning (ML) models [7; 8], and (3) deep learning (DL) models [9; 10]. All these three categories can achieve excellent house appliances sales forecasting with the help of sufficient sales data, and these data can be easily obtained in large cities, especially in megacities. However, in small towns, the sales and recycling network may be not established, and there is not sufficient historical house appliances sales data to train the complex predictive models. In addition, since the market demand in small towns may differ from that of large cities significantly, the complexity of predictions could be further improved. Therefore, it is necessary to propose a novel house appliances sales forecasting algorithm via small sample mining for small towns. Transfer learning (TL) is a powerful strategy in ML that is extensively employed for small sample learning. It leverages knowledge learned from one task to enhance the performance of another, related task [11]. This technique is especially effective when the source and target tasks share some correlations, as it can speed up the learning process and reduce the need for extensive data and computational resources. In order to address the challenge of limited data in small town home appliances sales, we propose a novel TL approach that combines the temporal convolutional network (TCN), long short-term memory (LSTM), and attention mechanism (called \"TransTLA\"). Firstly, we integrate the TCN and LSTM networks to capture both spatial and temporal correlations within sales data and extract meaningful features. Then, we employ attention mechanisms to effectively utilize the extracted features, enhancing the model's understanding of the sales data. Finally, we use TL techniques to address data scarcity and regional disparities. After one round of training, the model incorporates the feature discrepancy between the source domain and target domain into a composite loss function. This step highlights the model's strength in domain adaptation.