基于网络搜索数据的游客量组合预测模型

Multi-Models Combination Tourists Quantity Forecasting Based on Network Search Data

随着信息技术的不断发展,基于网络数据对事物近期发展态势预测成为热点.本文以北京市月度游客量预测为目标,以相关网络关键词搜索指数为自变量建立了BP神经网络、支持向量回归和随机森林三种单一预测模型,在此基础上构建组合模型以提高预测准确度.实验结果表明:基于GBDT建立的组合模型达到了较高的预测准确度,误差仅为3.16%,预测结果可以为旅游管理部门提供决策支持.

With the continuous development of information technology,the forecast of the recent development of things based on the network data has become a hotspot.In order to predict the monthly number of tourists in Beijing,this study established three kinds of single models with the search index of the relevant network keywords as independent variables：BP neural network,support vector regression,and random forest,and constructed a variety of combinatorial models to improve the prediction accuracy.The experimental results show that the combination of models based on GBDT have achieved higher prediction accuracy,the error is 3.16%.The forecast results can provide decision support for tourism management.