基于ARIMA-LSTM组合模型的短途运输货物量预测研究

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中图分类号：TP391.4；TP183 文献标识码：A 文章编号：2096-4706（2025）09-0043-06

Abstract： As an important part of modern urban logistics system， short-distance transportation directly affects the capacity scheduling and overall operation efficiency. Aiming at the violent fluctuation and nonlinear characteristics in the volume data， this paper designs a composite prediction framework combining AutoRegressive Integrated Moving Average （ARIMA） and Long Short-Term Memory （LSTM） neural network. By analyzing the time series characteristics of historical cargo volume data， the stationarity test is carried out and the preliminary trend model is established. The ARIMA model is used to capture the linear dynamic characteristics， and then the LSTM network is used to learn and correct the complex nonlinear structure in the modeling residuals. The experimental verification based on real logistics line data shows that the combined prediction model is superior to the traditional single model in trend fitting and error control， and can provide more accurate data support for resource allocation and scheduling decision-making in short-distance transportation.

Keywords： ARIMA; LSTM; cargo volume prediction; short-distance transportation; time series; combined model

0 引 言

随着电子商务的快速发展，网络购物已逐渐渗透到人们日常生活的各个方面，推动了快递物流行业的规模扩张与体系升级 [1]。（剩余5927字）