Tehnički vjesnik, Vol. 27 No. 2, 2020.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20191231071057
The Role of Internet Search Index for Tourist Volume Prediction Based on GDFM Model
Yiran Li
orcid.org/0000-0001-9170-5898
; School of Information Management, Wuhan University, No. 299 Bayi Road, Wuchang District, Wuhan City, Hubei Province, China
Mengyao Xu
; School of Information Management, Wuhan University, No. 299 Bayi Road, Wuchang District, Wuhan City, Hubei Province, China
Xuan Wen
; School of Information Management, Wuhan University, No. 299 Bayi Road, Wuchang District, Wuhan City, Hubei Province, China
Daomeng Guo
; 1) School of Economics and Management, Hubei Engineering University, No. 272 Jiaotong Road, Xiaonan District, Xiaogan City, Hubei Province, China; 2) School of Information Management, Wuhan University, No. 299 Bayi Road, Wuchang District, Wuhan City, Hube
Sažetak
Tourist volume is increasing with the expansion of the scale of tourism, and improving the prediction of tourist volume is helpful for tourism managers to make decisions. Internet search index can be applied to predict the behavior of users, which is widely used in the study of tourist volume prediction and infectious disease prediction. However, the high dimension and correlation of Internet search index tends to reduce the accuracy of the models, which increases the average prediction error of common time-series models. The dynamic factor model (DFM) proposed in our study can be used to solve the problem. This study selects 23 variables and introduces the generalized dynamic factor model (GDFM) to predict tourist volume. The model cannot only reduce the dimensionality of high-dimensional Internet search index data, but also reflects the dynamic correlation between Internet search index data. The results show that the prediction accuracy is improved in our method, and the prediction accuracy of tourist volume is improved by over 10%, with an average error of only 4.3% when compared with the neural network (NN) model. Our study not only provides implications for decision-makers to predict tourist volume timely and accurately, but also helps companies understand tourist’ behavior and make the best strategic decisions.
Ključne riječi
big data analysis; generalized dynamic factor model; internet search index; tourist volume prediction
Hrčak ID:
236814
URI
Datum izdavanja:
15.4.2020.
Posjeta: 1.800 *