| 客运量预测模糊时间序列和灰色模型的比较 |
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| 引用本文： | 覃频频,黄大明.客运量预测模糊时间序列和灰色模型的比较[J].武汉理工大学学报(交通科学与工程版),2007,31(4):591-594. |
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| 作者姓名： | 覃频频 黄大明 |
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| 作者单位： | 1. 西南交通大学交通运输学院,成都,610031;广西大学机械工程学院,南宁,530004
2. 广西大学机械工程学院,南宁,530004 |
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| 摘 要： | 基于模糊集理论在模糊时间序列分析的基础上分别建立铁路、公路及民航客运量模糊时间序列模型,并与基于灰色理论的GM(1,1),修正GM(1,1)和Markvo三个模型进行标杆对比,结果表明:模糊时间序列模型能有效提高Markvo模型的预测效果;模型的外推预测能力比Markvo模型强;模糊时间序列模型和灰色模型相比,传统ARIMA时间序列模型及人工神经网络模型具有不需要大量历史时间序列样本的特点.
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| 关 键 词： | 客运量 预测 模糊时间序列 |
| 修稿时间： | 2007-03-01 |
Comparison of Fuzzy Time Series and Grey Model to Passenger Volume Forecasting |
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| Qin Pinpin,Huang Daming.Comparison of Fuzzy Time Series and Grey Model to Passenger Volume Forecasting[J].journal of wuhan university of technology(transportation science&engineering),2007,31(4):591-594. |
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| Authors: | Qin Pinpin Huang Daming |
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| Institution: | College of Transportation, Southwest Jiaotong University, Chengdu 610031 ;College of Mechanical Engineering, Guangxi University, Nanning 530004 |
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| Abstract: | A fuzzy time series forecasting model is presented using fuzzy sets theory.GM(1,1),residual modification of GM(1,1) and Markov-chain model are also established based on grey theory.Four models are compared.The results indicate that fuzzy time series model is the best forecasting model for all three kinds of passenger volume short term forecasting when rail and air data show significant fluctuations and road data show a stable increase trend.Fuzzy time series model outperforms ARIMA and neural network model without long historical time series data. |
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| Keywords: | GM(1 1) |
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