This paper focuses on improving the accurate prediction of tourist capacity, which is the key to solve the contradiction of tourism economy development and ecological environment protection. EMD-BP integrated predictive model is proposed and uses Empirical Model Decompose method to decompose time-series data of visitors into several TMF, then forecasting each component by BP neural network. In order to shown the effective of our method, an empirical study of Jiuzhaigou is conducted, and the average error rate of EMD-BP integrated prediction model is 8.8%, among which the error within 1% accounts for 31.3% of the total predicted amount, the error within 5% accounts for 41.7%, the error above 10% accounts for 30.2% in slack seasons and 69.8% in busy seasons.
ShaF., ZhuF., GuoS.N., et al., Based on the EMD and PSO-BP neural network of short-term load forecasting, Advanced Materials Research614-615 (2012), 1872–1875.
2.
ZhangC. and ZhangJ., Neural network ensemble for chinese inbound tourism demand prediction, Scientia Geographica Sinica31(10) (2011), 1208–1212.
3.
ClaveriaO. and TorraS., Forecasting tourism demand to Catalonia: Neural networks vs. time series models, Economic Modelling36(36) (2014), 220–228.
4.
Suhartono and LeeM.H., Forecasting of tourist arrivals using subset, multiplicative or additive seasonal ARIMA model, Matematika2 (2011), 169–182.
5.
IsmailZ., FadzliM. and MdM.M.Z., Optimization of SARIMA model using genetic algorithm method in forecasting singapore tourist arrivals to malaysia, Applied Mathematical Sciences8(170) (2014), 8481–8491.
6.
LyuM.N., YangQ.S., YangN., et al., Tourist number prediction of historic buildings by singular spectrum analysis, Journal of Applied Statistics43(5) (2016), 827–846.
7.
BerenguerT.M., BerenguerJ.A.M., GarcíaM.E.B., et al., Models of artificial neural networks applied to demand forecasting in nonconsolidated tourist destinations, Methodology European Journal of Research Methods for the Behavioral & Social Sciences1(-1) (2015), 1–10.
8.
GohC. and LawR., Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention, Tourism Management23(5) (2002), 499–510.
9.
SmeralE. and WügerM., Does complexity matter? Methods for improving forecasting accuracy in tourism: The case of Austria, Journal of Travel Research44(1) (2005), 100–110.
10.
MoutinhoL., HuarngK.H., YuH.K., et al., Modeling and forecasting tourism demand: The case of flows from Mainland China to Taiwan, Service Business2(3) (2008), 219.
11.
KunhuangH., MoutinhoL., Huikuang YuT., et al., An advanced approach to forecasting tourism demand in taiwan, Journal of Travel & Tourism Marketing21(4) (2007), 15–24.
12.
NamL.K., Forecasting long-haul tourism demand for Hong Kong using error correction models, Applied Economics43(5) (2011), 527–549.
13.
TranX., Application of vector auto regressive model in tourism, e-Review of Tourism Research11(3) (2014), 34–49.
14.
TsaurR.C. and ChanS.F., Grey support vector regression model with applications to China tourists forecasting in taiwan, International Journal of Information & Management Sciences25(2) (2014), 121–138.
15.
LawR. and AuN., A neural network model to forecast Japanese demand for travel to Hong Kong, Tourism Management20(1) (1999), 89–97.
16.
ChenK.Y., Combining linear and nonlinear model in forecasting tourism demand, Expert Systems with Applications38(8) (2011), 10368–10376.
17.
LiaoZ.X., JinM.Z., LuoY.Y., et al., Research on prediction of tourists’ quantity in Jiuzhaigou Valley scenic based on ABR@G integration model, International Journal of Environment & Pollution51(3-4) (2013), 176–191.
18.
NguyenT.L., ShuM.H., HuangY.F., et al., Accurate forecasting models in predicting the inbound tourism demand in Vietnam, Journal of Statistics & Management Systems16(1) (2013), 25–43.
19.
YuY. and WangS.M., The forecasting research of beijing tourism demand based on the BP neural network, Applied Mechanics & Materials571-572 (2014), 128–131.
20.
WeiY. and ChenM.C., Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks, Transportation Research Part C Emerging Technologies21(1) (2012), 148–162.
21.
ClaveriaO. and TorraS., Forecasting tourism demand to Catalonia: Neural networks vs. time series models, Economic Modelling36(36) (2014), 220–228.
22.
TeixeiraJ.P. and FernandesP.O., Tourism time series forecast with artificial neural networks, Tékhne12(1–2) (2014), 26–36.
