基于ICAKELM的港口集装箱吞吐量预测模型

    An ICAKELM-Based Model to Predict Container Throughput of Ports

    • 摘要: 为了提高港口集装箱吞吐量预测的准确性与稳定性,在分析传统分解和集成的优缺点的基础上,提出ICEEMDAN-SE-ARIMA& ICAKELM-IKM预测模型,并将其用于上海港的月集装箱吞吐量预测。该模型首先利用ICEEMDAN分解港口集装箱吞吐量序列并分析其子序列的复杂程度,再使用样本熵检验子序列的复杂程度,分别使用ARIMA和帝国竞争优化核极限学习机(ICAKELM)对子序列进行预测,最后使用ICAKELM将各子序列的预测结果进行非线性集成,得出最终的预测结果。实证结果表明,本文所建立的分解集成人工智能模型预测效果显著优于传统的BP、ARIMA等单一模型,同时对于港口集装箱吞吐量短期预测有较高的准确性。

       

      Abstract: A model integrating multiple processing technologies is developed to improve the accuracy and the consistency of the prediction of the container throughput of a port.The ICEEMDAN is used to resolve a port's container throughput sequence and analyze the complexity of the sub-sequences.The complexity of subsequences is examined with sample empathy.The ARIMA(autoregressive integrated moving average) and the ICAKELM(KELM optimized with imperialist competitive algorithm) are used to do subsequence prediction respectively.The ICAKELM is used to do non-linear composition of all the subsequences and find the final prediction.Experiments show that the proposed method gives better short-term prediction than traditionally sole BP or ARIMA.

       

    /

    返回文章
    返回