ZHU Chang-feng1， WANG Qing-rong2， LIU Dao-kuan3， YE Qian-yun3
（1. School of Traffic and Transportation， Lanzhou Jiaotong University， Lanzhou 730070， China； 2. School of Electronic and Information Engineering, Lanzhou Jiaotong University， Lanzhou 730070， China；3. China Railway Fourth Survey and Design Institute Group Co., Ltd, Wuhan 430063， China）
Abstract： To eliminate the grey bias and improve ant-jamming performance of the standard grey-Markov forecasting model, a forecasting model based on wavelet packet decomposition and fuzzy grey Markov（FG-Markov） is proposed considering the characteristics of randomness and nonlinearility of freight volume forecasting. Firstly, based on the data analysis ability of wavelet packet to non-stationary random signal, wavelet packet decomposition is used to improve the analysis ability of data signal by decomposing historical freight volume data into wavelet packet component. On this basis, FG-Markov chain is proposed to obtain the transfer probability matrix of wavelet packet coefficients by introducing fuzzy grey variables, and forecast the freight volume by reconstructing wavelet packet coefficients. Finally, an example of Lanzhou railroad hub is carried out in order to testify the validity and applicability of this forecasting model. Compared with neural network model and other forecasting models, the proposed forecasting model can improve the forecasting accuracy under the same conditions. The forecasting accuracy of wavelet packet decomposition and FG-Markov is not only greater than that of any other single forecasting models, but also superior to that of other traditional combinational forecasting models, which can meet the actual requirements of freight volume forecasting.
Key words： freight volume forecasting； fuzzy grey model； wavelet packet； Markov chain
CLD number： TP273; U695.2doi： 10.3969/j.issn.1674-8042.2020.03.010
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基于小波去噪和FG-Markov的货运量预测
朱昌锋1， 王庆荣2， 刘道宽3， 叶前云3
（1. 兰州交通大学 交通运输学院， 甘肃 兰州 730070； 2. 兰州交通大学 电子与信息工程学院， 甘肃 兰州 730070； 3. 中铁第四勘察设计院集团有限公司 道路交通设计研究院， 湖北 武汉 430063）
摘要:为消除传统灰色马尔科夫预测模型的灰色偏差及抗干扰性能， 考虑货运量预测的随机性和非线性特征， 提出了基于小波变换和模糊灰色马尔科夫（FG-Markov）的货运量预测模型。 基于小波包对非平稳随机信号的数据分析能力， 运用小波包分解策略， 对货运量历史数据进行小波包分解。 在此基础上， 引入模糊灰色变量， 提出了模糊灰色马尔科夫链（FG-Markov）来获取小波包系数转移概率矩阵， 并通过重构小波包系数进行货运量预测。 为验证预测模型的有效性和精确度， 将其应用于兰州铁路枢纽集装箱货运量预测， 并与神经网络等预测模型进行了比较分析。 实例分析表明， 基于小波和FG-Markov的预测模型可以提高预测精度。
关键词:货运量预测； 模糊灰色模型； 小波包； 马尔科夫链
引用格式： ZHU Chang-feng, WANG Qing-rong, LIU Dao-kuan, et al. Forecasting freight volume based on wavelet denoising and FG-Markov. Journal of Measurement Science and Instrumentation, 2020, 11（3）： 267-275. ［doi： 10.3969／j.issn.1674-8042.2020.03.010］
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