Logistics demand forecasting method based on deep learning

Hui'e Lu

doi:10.1117/12.3011533

8 November 2023 Logistics demand forecasting method based on deep learning

Hui'e Lu

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 1292312 (2023) https://doi.org/10.1117/12.3011533
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

The new round of scientific and technological revolution and the continuous optimization of economic structure put forward higher requirements for the prediction of logistics demand. Aiming at the problems of low prediction accuracy of traditional algorithms, we propose a logistics demand prediction method based on deep learning. Specifically, a LSTM logistics demand forecasting model is established to realize more accurate logistics demand forecasting. From the perspective of data structure of forecasting model, we compare and evaluate LSTM forecasting model with traditional statistical methods, neural network methods, support vector regression machines and other methods in terms of time series and influencing factors. The experimental results show that the logistics demand forecasting model based on LSTM has good forecasting performance, with an average absolute percentage error of about 4% and good stability.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hui'e Lu "Logistics demand forecasting method based on deep learning", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 1292312 (8 November 2023); https://doi.org/10.1117/12.3011533

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