Forecasting Chinese tourist volume with search engine data

420 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Forecasting tourist arrivals with machine learning and internet search index (2017, methodology): “1) Internet Search Index We followed a five stages process in selecting candidate queries and constructing Internet search index [9], by means of the related searches function on Baidu Index and Google Trends: • We initially chose 24 basic search queries according to various aspects of tourist’s tri”
A Big Data Analytics Method for Tourist Behaviour Analysis (2017, neutral): “tourists’ interests, visited locations, tourists’ personal experience and be able to predict future tourism demand [75].”
New developments in tourism and hotel demand modeling and forecasting (2017, methodology): “data, Yang et al. (2015) also examined the value of Baidu (the largest search engine in China) data and demonstrated the potential of these data in improving forecasting”
The impact of COVID-19 on tourism sector in India (2020, methodology): “…like regression model, exponential smoothing (Athanasopoulos & Hyndman, 2008), Basic structural time series model (BSM) and causal structural time series model (STSM) (Kulendran & Witt, 2003; Turner & Witt, 2001), Autoregressive Moving Average with External Variables (ARMAX) (Yang et al., 2015).”