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| 旅游需求预测模型研究 | |
| 引用本文： | 覃频频,陆凯平,牙韩高. 旅游需求预测模型研究[J]. 铁道运输与经济, 2006, 28(8): 73-75 |
| 作者姓名： | 覃频频 陆凯平 牙韩高 |
| 作者单位： | 1. 西南交通大学,交通运输学院,四川,成都,610031 2. 桂林市交通局,广西,桂林,541002 3. 西南交通大学,经济管理学院,四川,成都,610031 |
| 摘 要： | 建立基于月度数据的桂林漓江旅游航班、运量及游客的需求预测模型,运用指数平滑、SARIMA和Elman人工神经网络3种方法,并采用平均绝对误差(MAE)、均方百分比误差(RMSE)和平均绝对百分比误差(MAPE)评价模型预测效果。预测实例表明Elman人工神经网络模型更能反映时间序列的波动性,更适合桂林漓江旅游需求预测。 |
| 关 键 词： | 旅游 需求预测 指数平滑 模型 研究 |
| 文章编号： | 1003-1421(2006)08-0073-03 |
| 收稿时间： | 2006-01-09 |
| 修稿时间： | 2006-03-31 |
Research on Forecasting Model of Tour Demand |
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| QIN Pin-pin,LU Kai-ping,YA Han-gao. Research on Forecasting Model of Tour Demand[J]. Rail Way Transport and Economy, 2006, 28(8): 73-75 | |
| Authors: | QIN Pin-pin LU Kai-ping YA Han-gao |
| Affiliation: | 1. School of Traffic and Transportation, South-west Jiaotong University, Chengdu, Sichuan 610031, China; 2. Guilin Traffic Bureau, Guilin, Guangxi 541002, China; 3. School of Economics and Management, South-west Jiaotong University, Chengdu, Sichuan 610031, China |
| Abstract: | This essay suggests to establish demand forecast model on number of scheduled boats, traffic volume and tourists in Lijiang, Guilin based on monthly historical data, employs the three time-series forecast techniques, namely exponential smoothing, SARIMA, and Elman artificial neural networks (ANN), and applyes MAE, RMSE and MAPE to evaluate the forecasting results. The research indicates that Elman neural network could reflect the wave of timing series better and is more suitable for tourist demand forecast of Lijiang, Guilin. |
| Keywords: | tourism demand forecast exponential smoothing model research |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |