This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Open AccessArticle
TransTLA: A Transfer Learning Approach with TCN-LSTM-Attention for Household Appliance Sales Forecasting in Small Towns
by
Zhijie Huang
Zhijie Huang and
Jianfeng Liu
Jianfeng Liu *
Department of Information and Communication Engineering, Tongji University, Shanghai 201804, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(15), 6611; https://doi.org/10.3390/app14156611 (registering DOI)
Submission received: 26 June 2024
/
Revised: 19 July 2024
/
Accepted: 27 July 2024
/
Published: 28 July 2024
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.
Share and Cite
MDPI and ACS Style
Huang, Z.; Liu, J.
TransTLA: A Transfer Learning Approach with TCN-LSTM-Attention for Household Appliance Sales Forecasting in Small Towns. Appl. Sci. 2024, 14, 6611.
https://doi.org/10.3390/app14156611
AMA Style
Huang Z, Liu J.
TransTLA: A Transfer Learning Approach with TCN-LSTM-Attention for Household Appliance Sales Forecasting in Small Towns. Applied Sciences. 2024; 14(15):6611.
https://doi.org/10.3390/app14156611
Chicago/Turabian Style
Huang, Zhijie, and Jianfeng Liu.
2024. "TransTLA: A Transfer Learning Approach with TCN-LSTM-Attention for Household Appliance Sales Forecasting in Small Towns" Applied Sciences 14, no. 15: 6611.
https://doi.org/10.3390/app14156611
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details
here.
Article Metrics
Article Access Statistics
For more information on the journal statistics, click
here.
Multiple requests from the same IP address are counted as one view.