摘要
针对港口集装箱吞吐量数据的复杂性特征,提出基于优化变分模态分解(OVMD)和核极限学习机(KELM)的集装箱吞吐量短期混合预测模型。首先,用汉佩尔辨识法(HI)剔除原始时间序列中的异常值,并把预处理之后的序列通过OVMD分解为多个特征明显的子模态。然后,为提高预测效率,将分解后的子模态按照样本熵(SE)值的大小分成高频低幅、中频中幅和低频高幅三类;同时,借助KELM中携带的小波、高斯和线性核函数捕捉具有不同特征子模态的趋势。最后,把所有子模态的预测结果线性相加得到最终的预测结果。以深圳港的月度集装箱吞吐量数据为样本进行实验,所提模型的平均绝对误差(MAE)达到0.9149,平均绝对百分比误差(MAPE)达到0.199%,均方根误差(RMSE)达到7.8860,决定系数(R2)为0.9944。与四种对比模型相比,所提出的模型在预测精度和效率上都具有一定的优势,同时克服了传统互补集成经验模态分解(CEEMD)和集成经验模态分解(EEMD)中容易出现的模态混叠问题以及极限学习机(ELM)中存在过拟合等问题,具有一定的实际应用潜力。
Aiming at the complexity of port container throughput data,a short-term hybrid prediction model of container throughput based on Optimal Variational Mode Decomposition(OVMD)and Kernel Extreme Learning Machine(KELM)was proposed.Firstly,the outliers were removed by Hampel Identifier(HI)from the original time series,and the preprocessed series was decomposed into several sub-modes with obvious characteristics by OVMD.Then,in order to improve the prediction efficiency,the decomposed sub-modes were divided into three categories according to the values of Sample Entropy(SE):high frequency low amplitude,medium frequency medium amplitude and low frequency high amplitude.At the same time,the wavelet,Gauss and linear kernel functions carried in KELM were used to capture the trends of sub-modes with different characteristics.Finally,the final prediction result was obtained by linearly adding the prediction results of all sub-modes together.Taking the monthly container throughput data at Shenzhen Port as a sample for empirical research,the proposed model has the Mean Absolute Error(MAE)of 0.9149,the Mean Absolute Percentage Error(MAPE)of 0.199%,the Root Mean Square Error(RMSE)of 7.8860 and the coefficient of determination(R2)of 0.9944.Compared with four comparison models,the proposed model has advantages in prediction accuracy and efficiency.At the same time,it overcomes the mode mixing problem in traditional Complementary Ensemble Empirical Mode Decomposition(CEEMD)and Ensemble Empirical Mode Decomposition(EEMD)as well as overfitting defect in Extreme Learning Machine(ELM),and has practical application potential.
作者
张丰婷
杨菊花
任金荟
金坤
ZHANG Fengting;YANG Juhua;REN Jinhui;JIN Kun(School of Traffic and Transportation,Lanzhou Jiaotong University,Lanzhou Gansu 730070,China;Safety Production Department of Lanzhou Freight Center,China Railway Lanzhou Bureau Group Company Limited,Lanzhou Gansu 730030,China)
出处
《计算机应用》
CSCD
北大核心
2022年第8期2333-2342,共10页
journal of Computer Applications
基金
甘肃省自然科学基金资助项目(21JR7RA287)
甘肃省教育厅“双一流”科研重点项目(GSSYLXM-04)。
关键词
集装箱吞吐量预测
样本熵
变分模态分解
核极限学习机
分解集成预测模型
container throughput prediction
Sample Entropy(SE)
Variational Mode Decomposition(VMD)
Kernel Extreme Learning Machine(KELM)
decomposition-ensemble prediction model
