Chuanchuan Teng, and Xizhuo Han

Supply Chain Logistics Demand Modeling and Abnormal Warning Based on Autoformer

Current methods have limited capabilities in accurately modeling and dynamically warning of anomalies in port supply chain logistics (SCLs) scenarios where long-term trends in logistics demand (LD) coexist with short-term disturbances. To improve the accuracy of LD forecasting and anomaly warning (AW), and enhance the stability and responsiveness of the supply chain (SC), this paper constructed a multi-scale demand forecasting and AW model based on Autoformer. First, multi-source logistics data was integrated and combined with the Autoformer trend-disturbance decomposition mechanism to achieve structured modeling of non-stationary demand sequences. Then, a multi-scale attention path was designed to extract periodic information at the daily, monthly, and quarterly levels, respectively, to enhance the model's perception of multi-frequency features. Finally, a residual-driven anomaly scoring function and a dynamic threshold strategy were combined to construct a multi-level AW mechanism to achieve accurate identification of logistics risk events of different intensities. Experimental results show that in LD modeling, the Autoformer achieves a final weighted average percentage wrror (WAPE) and root mean square error (RMSE) of 0.105, and a matching degree of over 0.900 for the changing trend of supply chain logistics demand (SCLD) for a 200 twenty-feet equivalent unit (TEU) SC. In terms of AW, the Autoformer achieves average false negative rate (FNR) and false positive rate (FPR) of 12.0% and 7.3%, respectively, with an average overall delay of 1.3 hours. The weighted Kappa coefficient for consist-ency in AW levels is 0.844. The results show that the proposed Autoformer model, on the port logistics dataset used, demonstrates certain improvements in demand forecasting and anomaly detection compared to the selected baseline model, providing a feasible solution reference for forecasting and early warning tasks in intelligent supply chain management.

Reference:

DOI: 10.36244/ICJ.2026.2.5

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Please cite this paper the following way:

Chuanchuan Teng, and Xizhuo Han "Supply Chain Logistics Demand Modeling and Abnormal Warning Based on Autoformer", Infocommunications Journal, Vol. XVIII, No 2, June 2026, pp. 32-44., https://doi.org/10.36244/ICJ.2026.2.5