Analysis on the Role of Daily Consumer Search Data in Forecasting Monthly Tourist Flow A Mixed Data Sampling Approach

DOI

10.2991/emle-18.2018.75How to use a DOI?

Keywords

MIDAS model; tourist flows; consumer search data; forecasting precision

Abstract

In order to evaluate the predictive ability of network search data of daily sampling frequency for monthly tourist flow, this paper predicts the monthly tourist flow of Chongqing, China. In consideration of the inconsistency of sampling frequency of network search data and tourist flow data, an autoregression mixed data sampling model (AR-MIDAS) is constructed for prediction to avoid the loss of information. This paper adopts factor analysis technology to extract the characteristic information contained in the consumer search data related to Chongqing tourism, and then puts the obtained comprehensive factor into the model for a prediction experiment. The research results show that AR-MIDAS model can improve the precision of monthly tourist flow prediction better than ARIMA and MIDAS prediction techniques. The research results can provide necessary reference for scientific decision-making of tourism related departments.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhang Binru
AU  - Pu Yulian
AU  - Hu Rong
AU  - Tang Runzhi
PY  - 2018/12
DA  - 2018/12
TI  - Analysis on the Role of Daily Consumer Search Data in Forecasting Monthly Tourist Flow A Mixed Data Sampling Approach
BT  - Proceedings of the 4th International Conference on Economics, Management, Law and Education (EMLE 2018)
PB  - Atlantis Press
SP  - 413
EP  - 416
SN  - 2352-5428
UR  - https://doi.org/10.2991/emle-18.2018.75
DO  - 10.2991/emle-18.2018.75
ID  - Binru2018/12
ER  -