Computer Science ›› 2018, Vol. 45 ›› Issue (1): 122-127.doi: 10.11896/j.issn.1002-137X.2018.01.020

Previous Articles     Next Articles

Study on Tourism Demand Forecasting Based on Improved Grey Model

LI Yao, CAO Han and MA Jing   

  • Online:2018-01-15 Published:2018-11-13

Abstract: Aiming at the tourism demand forecasting problems in Hainan Province,this paper proposed a novel dynamic optimal input subset fuzzy grey-Markov prediction model based on traditional grey-Markov model.This model firstly determines the optimal number of input subsets through input subset method according to the mean absolute percentage error of the prediction of GM (1,1) model,then calculates the membership vector and takes it as weight vector of Markov transfer matrix vector so that the forecast value can be revised through fuzzy set theory.An equal dimension increasing dynamic grey prediction model was created based on the characteristic of the passage of time,which enables us to predict the tourism demand.The number of tourists received by hotels in Hainan Province is taken as an example to show that the model can effectively improve the accuracy of forecasting data.

Key words: Forecast,Grey model,Fuzzy set,Hainan province

[1] YANG L X,YIN S L.A Literature Review and Applicationabout Artificial Intelligence in Tourism Demand Forecasting[J].Tourism Tribune,2008(9):17-22.(in Chinese) 杨立勋,殷书炉.人工智能方法在旅游预测中的应用及评析[J].旅游学刊,2008(9):17-22.
[2] DAI B,ZHANG P Y.Tourism demand forecast based on the improved double parallel dynamic process neural network[J].Journal of Changchun University,2010(1):21-23.(in Chinese) 戴冰,张培茵.基于改进的双并联动态过程神经网络的旅游需求预测[J].长春大学学报,2010(1):21-23.
[3] TEIXEIRA J P,FERNANDES P O.Tourism time series forecast with artificial neural networks[J].Tekhne,2014,12(1):26-36.
[4] CLAVERIA O,TORRA S.Forecasting tourism demand to Cata-lonia:Neural networks vs.time series models[J].Economic Modelling,2014,36:220-228.
[5] ZENG D L,YU K,ZHAO Q J.Tourism demand forecastingbased on Grey system theory and Markov adjustment-talking Yunnan tourism market as an example[J].Journal of Chongqing Technology and Bussiness University (Natural Science Edition),2016(4):58-68.(in Chinese) 曾冬玲,喻科,赵清俊.基于灰色理论和马尔科夫修正的旅游需求预测——以云南省旅游市场为例[J].重庆工商大学学报(自然科学版),2016(4):58-68.
[6] LI K Z,LI Z W,ZHAO L J.An optimized GM(1,1) deformation forecasting model based on Markov theory[J].Science of Surveying and Mapping,2016(8):1-5.(in Chinese) 李克昭,李志伟,赵磊杰.马尔科夫理论的优化灰色模型预测建模[J].测绘科学,2016(8):1-5.
[7] ZHANG W Y,LUAN J.Tourist amount forecasting methodbased on improving GM-Markov modeling and its application[J].Computer Engineering and Applications,2016,52(13):110-114,151.(in Chinese) 张文宇,栾婧.改进GM-Markov模型的游客量预测方法及其应用[J].计算机工程与应用,2016,52(13):110-114,1.
[8] HUANG Y F,WANG C N,DANG H S,et al.Predicting the trend of taiwan’s electronic paper industry by an effective combined grey model[J].Sustainability,2015,7(8):10664-10683.
[9] 刘思峰,杨英杰,吴利丰.灰色系统理论及其应用[M].北京:科学出版社,2014.
[10] LIU E,WANG Q,GE X,et al.Dynamic Discrete GM (1,1) Model and Its Application in the Prediction of Urbanization Conflict Events[J].Discrete Dynamics in Nature and Society,2016,2016(1):1-10.
[11] WANG Z X,HAO P.An improved grey multivariable model for predicting industrial energy consumption in China[J].Applied Mathematical Modelling,2016,40(11):5745-5758.
[12] ZHAO H,GUO S.An optimized grey model for annual power load forecasting[J].Energy,2016,107:272-286.
[13] REZAEIANZADEH M,STEIN A,COX J P.Drought forecasting using markov chain model and artificial neural networks[J].Water Resources Management,2016,30(7):2245-2259.
[14] XIE N,YUAN C,YANG Y.Forecasting China’s energy de-mand and self-sufficiency rate by grey forecasting model and markov model[J].International Journal of Electrical Power & Energy Systems,2015,66:1-8.
[15] BAHRAMI S,HOOSHMAND R A,PARASTEGARI M.Short term electric load forecasting by wavelet transform and grey model improved by pso (particle swarm optimization) algorithm[J].Energy,2014,72:434-442.
[16] LU S L,TSAI C F.Petroleum demand forecasting for Taiwan using modified fuzzy-grey algorithms[J].Expert Systems,2016,33(1):60-69.
[17] GUPTA M,GAO J,AGGARWAL C C.Outlier detection fortemporal data:A survey[J].IEEE Trans on Knowledge and Data Engineering,2014,26(9):2250-2267.
[18] WU L,LIU S,YANG Y.Grey double exponential smoothingmodel and its application on pig price forecasting in China[J].Applied Soft Computing,2016,39:117-123.
[19] SAMET H,MOJALLAL A.Enhancement of electric arc furnace reactive power compensation using grey-markov prediction method[J].IET generation,transmission & distribution,2014,8(9):1626-1636.
[20] LIU C,SHU T,CHEN S,et al.An improved grey neural network model for predicting transportation disruptions[J].Expert Systems with Applications,2016,45:331-340.
[21] SUN X,SUN W,WANG J,et al.Using a grey-markov modeloptimized by cuckoo search algorithm to fo-recast the annual foreign tourist arrivals to China[J].Tourism Management,2016,52:369-379.
[22] CHEN M Y,CHEN B T.A hybrid fuzzy time series modelbased on granular computing for stock price forecasting[J].Information Sciences,2015,294:227-241.
[23] CHEN M Y,CHEN B T.A hybrid fuzzy time series modelbased on granular computing for stock price forecasting[J].Information Sciences,2015,294:227-241.
[24] CHEN M Y,CHEN B T.Online fuzzy time series analysis based on entropy discretization and a fast fourier transform[J].Applied Soft Computing,2014,14:156-166.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!