Integrating search engine big data into forecasting models has been proven to enhance prediction accuracy. However, the challenge of multicollinearity among search query variables limits the effective use of such data. To address this, this study proposes a multicollinearity-eliminating feature extraction (MEFE) method, integrated with a backpropagation neural network (BPNN), to create the MEFE-BPNN framework for tourism demand forecasting. Empirical results show that the proposed framework consistently outperforms traditional time series models and conventional feature extraction techniques in prediction accuracy. These findings highlight the MEFE-BPNN framework’s superiority and its potential as an advanced tool for big data-driven tourism demand forecasting.
AaronsonDBraveSAButtersRA, et al. (2022) Forecasting unemployment insurance claims in realtime with google trends. International Journal of Forecasting38(2): 567–581.
2.
AbdiHWilliamsLJ (2010) Principal component analysis. WIREs Computational Statistics2(4): 433–459.
AmpountolasA (2019) Forecasting hotel demand uncertainty using time series Bayesian VAR models. Tourism Economics25(5): 734–756.
5.
BenestyJChenJHuangY, et al. (2009) Pearson correlation coefficient. In: CohenIHuangYChenJ, et al. (eds) Noise Reduction in Speech Processing. Springer, 1–4.
6.
BooneTGaneshanRJainA, et al. (2019) Forecasting sales in the supply chain: consumer analytics in the big data era. International Journal of Forecasting35(1): 170–180.
7.
ChenJYangJHuangS, et al. (2023) Forecasting tourist arrivals for Hainan Island in China with decomposed broad learning before the COVID-19 pandemic. Entropy25(2): 338.
8.
CiliaNDDe StefanoCFontanellaF, et al. (2019) Variable-length representation for EC-Based feature selection in high-dimensional data. In: KaufmannPCastilloPA (eds) Applications of Evolutionary Computation. Springer International Publishing, 325–340.
9.
DongXLianYLiuY (2018) Small and multi-peak nonlinear time series forecasting using a hybrid back propagation neural network. Information Sciences424: 39–54.
10.
DritsakisN (2004) Cointegration analysis of German and British tourism demand for Greece. Tourism Management25(1): 111–119.
11.
FengYLiGSunX, et al. (2019) Forecasting the number of inbound tourists with google trends. Procedia Computer Science162: 628–633.
12.
FontiVBelitserE (2017) Feature selection using lasso. VU Amsterdam Research Paper in Business Analytics30: 1–25.
13.
FuWKnightK (2000) Asymptotics for lasso-type estimators. Annals of Statistics28(5): 1356–1378.
14.
GeigerBCKubinG (2012) Relative information loss in the PCA. In: 2012 IEEE Information Theory Workshop, pp. 562–566.
15.
GuyonIWestonJBarnhillS, et al. (2002) Gene selection for cancer classification using support vector machines. Machine Learning46(1): 389–422.
16.
HeKJiLWuCWD, et al. (2021) Using SARIMA–CNN–LSTM approach to forecast daily tourism demand. Journal of Hospitality and Tourism Management49: 25–33.
17.
HuZBaoYXiongT, et al. (2015) Hybrid filter–wrapper feature selection for short-term load forecasting. Engineering Applications of Artificial Intelligence40: 17–27.
18.
HuMQiuRTRWuDC, et al. (2021a) Hierarchical pattern recognition for tourism demand forecasting. Tourism Management84: 104263.
HuMXiaoMLiH (2021) Which search queries are more powerful in tourism demand forecasting: searches via mobile device or PC?International Journal of Contemporary Hospitality Management33(6): 2022–2043.
21.
HuMLiHSongH, et al. (2022) Tourism demand forecasting using tourist-generated online review data. Tourism Management90: 104490.
22.
HuangBHaoH (2021) A novel two-step procedure for tourism demand forecasting. Current Issues in Tourism24(9): 1199–1210.
23.
HuangXZhangLDingY (2017) The Baidu index: uses in predicting tourism flows –A case study of the forbidden city. Tourism Management58: 301–306.
24.
JebliIBelouadhaF-ZKabbajMI, et al. (2021) Prediction of solar energy guided by pearson correlation using machine learning. Energy224: 120109.
25.
JiaoEXChenJL (2019) Tourism forecasting: a review of methodological developments over the last decade. Tourism Economics25(3): 469–492.
26.
JolliffeITCadimaJ (2016) Principal component analysis: a review and recent developments. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences374(2065): 20150202.
27.
JovićABrkićKBogunovićN (2015) A review of feature selection methods with applications In: 2015 38th International Convention on Information and Communication Technology. Electronics and Microelectronics (MIPRO), 1200–1205.
28.
LawRLiGFongDKC, et al. (2019) Tourism demand forecasting: a deep learning approach. Annals of Tourism Research75: 410–423.
29.
LiXPanBLawR, et al. (2017) Forecasting tourism demand with composite search index. Tourism Management59: 57–66.
30.
LiYCaoH (2018) Prediction for tourism flow based on LSTM neural network. Procedia Computer Science129: 277–283.
31.
LiSChenTWangL, et al. (2018) Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index. Tourism Management68: 116–126.
32.
LiXLiHPanB, et al. (2021) Machine learning in internet search query selection for tourism forecasting. Journal of Travel Research60(6): 1213–1231.
33.
LiJZhuDLiC (2022) Comparative analysis of BPNN, SVR, LSTM, random forest, and LSTM-SVR for conditional simulation of non-gaussian measured fluctuating wind pressures. Mechanical Systems and Signal Processing178: 109285.
34.
LiuH-HChangL-CLiC-W, et al. (2018) Particle swarm optimization-based support vector regression for tourist arrivals forecasting. Computational Intelligence and Neuroscience2018: e6076475.
35.
LiuHLiuYLiG, et al. (2021) Tourism demand nowcasting using a LASSO-MIDAS model. International Journal of Contemporary Hospitality Management33(6): 1922–1949.
36.
LiuYMuYChenK, et al. (2020) Daily activity feature selection in smart homes based on pearson correlation coefficient. Neural Processing Letters51(2): 1771–1787.
37.
LiuYFengGTsuiK-L, et al. (2021b) Forecasting influenza epidemics in Hong Kong using google search queries data: a new integrated approach. Expert Systems with Applications185: 115604.
38.
LuoFYangYZhongJ, et al. (2007) Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory. BMC Bioinformatics8(1): 299.
39.
MooreWR (2010) The impact of climate change on Caribbean tourism demand. Current Issues in Tourism13(5): 495–505.
40.
Nguyen-DaTLiY-MPengC-L, et al. (2023) Tourism demand prediction after COVID-19 with deep learning hybrid CNN–LSTM—Case study of Vietnam and provinces. Sustainability15(9): 7179.
41.
NiTWangLZhangP, et al. (2021) Daily tourist flow forecasting using SPCA and CNN‐LSTM neural network. Concurrency and Computation: Practice and Experience33(5): e5980.
42.
OnafoworaOAOwoyeO (2012) Modelling international tourism demand for the Caribbean. Tourism Economics18(1): 159–180.
43.
PadhiSSPatiRK (2017) Quantifying potential tourist behavior in choice of destination using google trends. Tourism Management Perspectives24: 34–47.
44.
PengBSongHCrouchGI (2014) A meta-analysis of international tourism demand forecasting and implications for practice. Tourism Management45: 181–193.
45.
RiedmillerM (1994) Advanced supervised learning in multi-layer perceptrons—from backpropagation to adaptive learning algorithms. Computer Standards & Interfaces16(3): 265–278.
46.
RiedmillerMBraunH (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. IEEE International Conference on Neural Networks1: 586–591.
47.
RodgersJLNicewanderWA (1988) Thirteen ways to look at the correlation coefficient. The American Statistician42(1): 59–66.
48.
SmeralE (2010) Impacts of the world recession and economic crisis on tourism: forecasts and potential risks. Journal of Travel Research49(1): 31–38.
49.
SmeralESongH (2015) Varying elasticities and forecasting performance. International Journal of Tourism Research17(2): 140–150.
50.
SongHLinS (2010) Impacts of the financial and economic crisis on tourism in Asia. Journal of Travel Research49(1): 16–30.
51.
SongHQiuRTRParkJ (2019) A review of research on tourism demand forecasting: launching the annals of tourism research curated collection on tourism demand forecasting. Annals of Tourism Research75: 338–362.
52.
TanKYanWZhangL, et al. (2021) Specific emitter identification based on software-defined radio and decision fusion. IEEE Access9: 86217–86229.
53.
TangLZhangCLiT, et al. (2021) A novel BEMD-based method for forecasting tourist volume with search engine data. Tourism Economics27(5): 1015–1038.
54.
ToloşiLLengauerT (2011) Classification with correlated features: unreliability of feature ranking and solutions. Bioinformatics27(14): 1986–1994.
55.
UniejewskiBMarcjaszGWeronR (2019) Understanding intraday electricity markets: variable selection and very short-term price forecasting using LASSO. International Journal of Forecasting35(4): 1533–1547.
56.
WangYWangLChangQ, et al. (2020) Effects of direct input–output connections on multilayer perceptron neural networks for time series prediction: L. Wang. Soft Computing24: 4729–4738.
57.
WenLLiuCSongH (2019) Forecasting tourism demand using search query data: a hybrid modelling approach. Tourism Economics25(3): 309–329.
58.
WongFS (1991) Time series forecasting using backpropagation neural networks. Neurocomputing2(4): 147–159.
59.
WuS (2019) Forecasting museum visitor behaviors using deep learning. 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI). IEEE, 186–190.
60.
XieGQianYWangS (2021) Forecasting Chinese cruise tourism demand with big data: an optimized machine learning approach. Tourism Management82: 104208.
61.
XuQZhuoXJiangC, et al. (2018) Group penalized unrestricted mixed data sampling model with application to forecasting US GDP growth. Economic Modelling75: 221–236.
62.
YanKZhangD (2015) Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sensors and Actuators B: Chemical212: 353–363.
63.
YangYPanBSongH (2014) Predicting hotel demand using destination marketing organization’s web traffic data. Journal of Travel Research53(4): 433–447.
64.
YangXPanBEvansJA, et al. (2015) Forecasting Chinese tourist volume with search engine data. Tourism Management46: 386–397.
65.
YangYFanYJiangL, et al. (2022) Search query and tourism forecasting during the pandemic: when and where can digital footprints be helpful as predictors?Annals of Tourism Research93: 103365.
66.
ZhangCTianY-X (2022) Forecast daily tourist volumes during the epidemic period using COVID-19 data, search engine data and weather data. Expert Systems with Applications210: 118505.
67.
ZhangBHuangXLiN, et al. (2017) A novel hybrid model for tourist volume forecasting incorporating search engine data. Asia Pacific Journal of Tourism Research22(3): 245–254.
68.
ZhangBPuYWangY, et al. (2019) Forecasting hotel accommodation demand based on LSTM model incorporating internet search index. Sustainability11(17): 4708, Article 17.
69.
ZhangCTianY-XFanZ-P (2022) Forecasting sales using online review and search engine data: a method based on PCA–DSFOA–BPNN. International Journal of Forecasting38(3): 1005–1024.
70.
ZhouHDengZXiaY, et al. (2016) A new sampling method in particle filter based on pearson correlation coefficient. Neurocomputing216: 208–215.
