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- W2916816215 abstract "为提高交叉口短时交通流预测精度，以历史交通流量数据为基础，提出一种基于自适应噪声完整集成经验模态分解(complete ensemble empirical mode decomposition with adaptive noise，CEEMDAN)-排列熵(permutation entropy，PE)-在线序贯极限学习机(online sequential extreme learning machine，OSELM)组合预测模型(CEEMDAN-PE-OSELM).首先对交通流历史时间序列数据进行CEEMDAN分解，得到多个本征模态函数(intrinsic mode function，IMF)分量；通过PE算法对IMF分量进行重组，形成具有复杂度差异的重组子序列.然后，分别构建重组子序列OSELM预测模型，将预测结果相加得到最终预测流量.最后选取一实际交叉口，进行模型验证分析.结果表明：CEEMDAN-PE-OSELM模型的MAE、MAPE和MSE的值均低于其他模型，预测误差最小；EC值为0.963，高于ARIMA模型的EC值(0.898)，最接近于1，预测精度最高，稳定性最好.就同一预测模型而言，经过CEEMDAN-PE处理的模型的各项误差明显降低，预测精度有所提高." @default.
- W2916816215 created "2019-03-02" @default.
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- W2916816215 date "2018-03-30" @default.
- W2916816215 modified "2023-09-23" @default.
- W2916816215 title "交叉口短时流量CEEMDAN-PE-OSELM预测模型" @default.
- W2916816215 doi "https://doi.org/10.11918/j.issn.0367-6234.201703102" @default.
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