A Hybrid Temporal Convolutional Network and Transformer Model for Accurate and Scalable Sales Forecasting
Accurate product sales forecasting is critical for inventory management, pricing strategies, and supply chain optimization in the retail industry. This article proposes a novel deep learning architecture that integrates Temporal Convolutional Networks (TCNs) with Transformer-based attention mechanis...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Computer Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10870315/ |
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| author | Rafi MD AL Gourab Nicholas Rodrigues Nazmul Hossain Mir MD Shahriar Mahmud Bhuiyan MD M. F. Mridha MD Rashedul Islam Yutaka Watanobe |
| author_facet | Rafi MD AL Gourab Nicholas Rodrigues Nazmul Hossain Mir MD Shahriar Mahmud Bhuiyan MD M. F. Mridha MD Rashedul Islam Yutaka Watanobe |
| author_sort | Rafi MD AL |
| collection | DOAJ |
| description | Accurate product sales forecasting is critical for inventory management, pricing strategies, and supply chain optimization in the retail industry. This article proposes a novel deep learning architecture that integrates Temporal Convolutional Networks (TCNs) with Transformer-based attention mechanisms to capture both short-term and long-term dependencies in time-series sales data. Utilizing the Favorita Grocery Sales Forecasting dataset, our hybrid TCN Transformer model demonstrates superior performance over existing models by incorporating external factors such as holidays, promotions, oil prices, and transaction data. The model achieves state-of-the-art results with a Mean Absolute Error (MAE) of 2.01, Root Mean Squared Error (RMSE) of 2.81, and a Weighted Mean Absolute Percentage Error (wMAPE) of 4.22%, significantly outperforming other leading models such as LSTM, GRU, and TFT. Extensive cross-validation confirms the robustness of our model, achieving consistently high performance across multiple folds. |
| format | Article |
| id | doaj-art-3247ca274ad6498f9c5dde2d2cc686fc |
| institution | DOAJ |
| issn | 2644-1268 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Computer Society |
| spelling | doaj-art-3247ca274ad6498f9c5dde2d2cc686fc2025-08-20T03:15:47ZengIEEEIEEE Open Journal of the Computer Society2644-12682025-01-01638039110.1109/OJCS.2025.353857910870315A Hybrid Temporal Convolutional Network and Transformer Model for Accurate and Scalable Sales ForecastingRafi MD AL0https://orcid.org/0009-0000-0115-7032Gourab Nicholas Rodrigues1https://orcid.org/0009-0000-4119-1753Nazmul Hossain Mir MD2https://orcid.org/0009-0001-7390-3046Shahriar Mahmud Bhuiyan MD3https://orcid.org/0009-0009-6126-6404M. F. Mridha4https://orcid.org/0000-0001-5738-1631MD Rashedul Islam5https://orcid.org/0000-0001-8676-6338Yutaka Watanobe6https://orcid.org/0000-0002-0030-3859Washington University of Science and Technology, Alexandria, VA, USAWashington University of Science and Technology, Alexandria, VA, USAWashington University of Science and Technology, Alexandria, VA, USAWashington University of Science and Technology, Alexandria, VA, USADepartment of Computer Science and Engineering, American International University-Bangladesh, Dhaka, BangladeshDepartment of Computer Science and Engineering, University of Asia Pacific, Dhaka, BangladeshSchool of Computer Science and Engineering, University of Aizu, Aizu-wakamatsu, JapanAccurate product sales forecasting is critical for inventory management, pricing strategies, and supply chain optimization in the retail industry. This article proposes a novel deep learning architecture that integrates Temporal Convolutional Networks (TCNs) with Transformer-based attention mechanisms to capture both short-term and long-term dependencies in time-series sales data. Utilizing the Favorita Grocery Sales Forecasting dataset, our hybrid TCN Transformer model demonstrates superior performance over existing models by incorporating external factors such as holidays, promotions, oil prices, and transaction data. The model achieves state-of-the-art results with a Mean Absolute Error (MAE) of 2.01, Root Mean Squared Error (RMSE) of 2.81, and a Weighted Mean Absolute Percentage Error (wMAPE) of 4.22%, significantly outperforming other leading models such as LSTM, GRU, and TFT. Extensive cross-validation confirms the robustness of our model, achieving consistently high performance across multiple folds.https://ieeexplore.ieee.org/document/10870315/Sales forecastingtime-series forecastingtemporal convolutional networkstransformersand deep learning |
| spellingShingle | Rafi MD AL Gourab Nicholas Rodrigues Nazmul Hossain Mir MD Shahriar Mahmud Bhuiyan MD M. F. Mridha MD Rashedul Islam Yutaka Watanobe A Hybrid Temporal Convolutional Network and Transformer Model for Accurate and Scalable Sales Forecasting IEEE Open Journal of the Computer Society Sales forecasting time-series forecasting temporal convolutional networks transformers and deep learning |
| title | A Hybrid Temporal Convolutional Network and Transformer Model for Accurate and Scalable Sales Forecasting |
| title_full | A Hybrid Temporal Convolutional Network and Transformer Model for Accurate and Scalable Sales Forecasting |
| title_fullStr | A Hybrid Temporal Convolutional Network and Transformer Model for Accurate and Scalable Sales Forecasting |
| title_full_unstemmed | A Hybrid Temporal Convolutional Network and Transformer Model for Accurate and Scalable Sales Forecasting |
| title_short | A Hybrid Temporal Convolutional Network and Transformer Model for Accurate and Scalable Sales Forecasting |
| title_sort | hybrid temporal convolutional network and transformer model for accurate and scalable sales forecasting |
| topic | Sales forecasting time-series forecasting temporal convolutional networks transformers and deep learning |
| url | https://ieeexplore.ieee.org/document/10870315/ |
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