Autoformer-Based Sales and Inventory Forecasting for Cross-Border E-commerce: A Time Series Deep Learning Approach
DOI:
https://doi.org/10.71222/kpqfx694Keywords:
Autoformer, cross-border e-commerce, sales forecasting, inventory management, time series prediction, deep learningAbstract
Abstract. Accurate forecasting of product sales and inventory is a critical task for cross-border e-commerce platforms, where demand volatility, long logistics cycles, and dynamic pricing present significant challenges for efficient supply chain management. Traditional statistical and short-term machine learning models often fail to capture long-term dependencies and complex seasonal variations in sales data, leading to inaccurate demand planning and inefficient inventory allocation. To address this limitation, we propose a forecasting framework based on the Autoformer model, a deep learning architecture designed for long-sequence time series prediction. The model leverages series decomposition blocks to separate trend and seasonal components, and employs an auto-correlation mechanism to enhance the capture of periodic demand patterns in product sales and inventory turnover. We evaluate the framework on a real-world cross-border e-commerce dataset comprising transaction-level order volume, prices, inventory records, and external market indicators such as exchange rates and shipping costs. Experimental results show that the proposed Autoformer-based approach achieves superior forecasting accuracy compared with baseline models including LSTM, Transformer, and Informer. Specifically, our model reduces prediction error with a Mean Absolute Error (MAE) of 18.6 and a Root Mean Square Error (RMSE) of 25.4, representing a 17.3% improvement over the best-performing baseline. These findings highlight the potential of Autoformer for enhancing sales forecasting, reducing stockouts, and improving inventory turnover in cross-border e-commerce platforms, thereby supporting more effective logistics management and strategic decision-making.References
1. G. E. P. Box, G. M. Jenkins, G. C. Reinsel, et al., Time series analysis: forecasting and control. John Wiley & Sons, 2015.
2. R. Hyndman, A. Koehler, K. Ord, et al., Forecasting with exponential smoothing: the state space approach. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008.
3. E. A. Silver, D. F. Pyke, R. Peterson, Inventory management and production planning and scheduling. New York: Wiley, 1998.
4. T. Chen, "XGBoost: A Scalable Tree Boosting System," Cornell University, 2016.
5. R. Carbonneau, K. Laframboise, R. Vahidov, "Application of machine learning techniques for supply chain demand forecasting," European Journal of Operational Research, vol. 184, no. 3, pp. 1140–1154, 2008.
6. K. J. Ferreira, B. H. A. Lee, D. Simchi-Levi, "Analytics for an online retailer: Demand forecasting and price optimization," Manufacturing & Service Operations Management, vol. 18, no. 1, pp. 69–88, 2016.
7. A. Graves, "Long short-term memory," Supervised Sequence Labelling with Recurrent Neural Networks, pp. 37–45, 2012.
8. K. Bandara, C. Bergmeir, S. Smyl, "Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach," Expert Systems with Applications, vol. 140, p. 112896, 2020.
9. A. Vaswani, N. Shazeer, N. Parmar, et al., "Attention is all you need," Advances in Neural Information Processing Systems, vol. 30, 2017.
10. B. Lim, S. Ö. Arık, N. Loeff, et al., "Temporal fusion transformers for interpretable multi-horizon time series forecasting," International Journal of Forecasting, vol. 37, no. 4, pp. 1748–1764, 2021.
11. H. Zhou, S. Zhang, J. Peng, et al., "Informer: Beyond efficient transformer for long sequence time-series forecasting," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 12, pp. 11106–11115, 2021.
12. H. Wu, J. Xu, J. Wang, et al., "Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting," Advances in Neural Information Processing Systems, vol. 34, pp. 22419–22430, 2021.
