集装箱吞吐量预测对港口企业运营及决策具有重要的作用。传统集装箱吞吐量预测方法存在预测精度不高的缺点。为解决这一问题，提出了一种考虑季节性和不确定性的SARIMA-XGBoost组合预测方法。针对集装箱吞吐量的季节性特征，选取季节性自回归移动平均模型（seasonal autoregressive integrated moving average model, SARIMA）捕捉周期性特征和线性特征；针对集装箱吞吐量中的不确定性因素，选取极致梯度提升树算法（extreme gradient boosting, XGBoost）自适应学习时间序列数据中的复杂模式和非线性特征。通过选取优化指标并计算分配权重的方式实现了预测模型中线性和非线性特征的有效融合，从而提升预测精度。通过对深圳港2013—2022年集装箱吞吐量月度数据进行实证研究和对比分析，结果表明SARIMA-XGBoost组合方法预测精度最高、稳定性好，验证了该组合方法在集装箱吞吐量预测中的有效性。

Container throughput forecasting plays a key role for port enterprises in operation management and decision-making. Traditional forecasting methods have low forecasting accuracy. To address this issue, a SARIMA-XGBoost combination method was proposed considering seasonality and uncertainty. The seasonal autoregressive integrated moving average model (SARIMA) model was employed to capture periodic and linear features related to container throughput seasonality, while the extreme gradient boosting (XGBoost) algorithm was utilized to adaptively learn complex patterns and nonlinear features within the time series, further mitigating the impact of uncertainty factors. Through the selection of optimization index and the calculation of corresponding weights, the effective combination of linear and nonlinear features in the forecasting model was proposed, leading to improved forecasting accuracy. Empirical research and comparative analysis were conducted by using monthly data on container throughput at Shenzhen Port from 2013 to 2022. The results demonstrate that the SARIMA-XGBoost combination method exhibits superior forecasting accuracy and stability, thus validating its effectiveness in container throughput forecasting.