Forecasting tourism demand using search query data: A hybrid modelling approach

Wen, Long; Liu, Chang; Song, Haiyan

doi:10.1177/1354816618768317

Cited by 46 publications

(37 citation statements)

References 61 publications

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“…Some research in the field of tourism has attempted to introduce search query data analytics (Fesenmaier et al, 2011; Volchek et al, 2018). Most researchers want to examine the accuracy of search query data in forecasting (Bokelmann & Lessmann, 2019; Wen et al, 2019). For instance, Pan et al (2012) first investigate the performance of search query data in predicting demand for hotel rooms, and find that prediction accuracy has increased significantly.…”

Section: Literature Reviewmentioning

confidence: 99%

A Study On The Influencing Factors Of Tourism Demand From Mainland China To Hong Kong

Liu¹,

Liu

Yong-lian

2020

Journal of Hospitality & Tourism Research

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Tourism research increasingly uses search query data to forecast demand, but the literature rarely explores the mechanisms of the factors influencing demand. A time-varying parameter factor vector auto-regression model is constructed based on Baidu Index on six aspects (dining, shopping, transportation, tours, attractions, and lodging) of tourism demand from January 2011 to March 2019. The model can quantitatively and comprehensively analyze the mechanisms of tourism demand and its six important influencing factors, and can provide suggestions for subsequent planning, construction, and services in the tourism industry. The empirical results show that the relationship between the six factors and domestic tourism demand is time-varying. Dining, attractions, and shopping have a driving effect on tourism demand, and are thus stimulative factors; transportation, tours, and lodging hinder the growth of tourism demand, and are thus baffle factors.

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Section: Literature Reviewmentioning

confidence: 99%

A Study On The Influencing Factors Of Tourism Demand From Mainland China To Hong Kong

Liu¹,

Liu

Yong-lian

2020

Journal of Hospitality & Tourism Research

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“…Camacho and Pacce (2018) use a dynamic factor approach to produce real-time predictions for the overnight stays of travelers in Spain. Wen et al (2019) combine the linear and nonlinear features of component models and claim that the new “hybrid” model is better than other models when forecasting tourist arrivals in Hong Kong from mainland China.…”

Section: Introductionmentioning

confidence: 99%

Forecasting tourist arrivals: Google Trends meets mixed-frequency data

Havránek

Zeynalov

2019

Tourism Economics

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In this article, we examine the usefulness of Google Trends data in predicting monthly tourist arrivals and overnight stays in Prague during the period between January 2010 and December 2016. We offer two contributions. First, we analyze whether Google Trends provides significant forecasting improvements over models without search data. Second, we assess whether a high-frequency variable (weekly Google Trends) is more useful for accurate forecasting than a low-frequency variable (monthly tourist arrivals) using mixed-data sampling (MIDAS). Our results suggest the potential of Google Trends to offer more accurate predictions in the context of tourism: we find that Google Trends information, both 2 months and 1 week ahead of arrivals, is useful for predicting the actual number of tourist arrivals. The MIDAS forecasting model employing weekly Google Trends data outperforms models using monthly Google Trends data and models without Google Trends data.

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“…Raman et al, [21] analyzed statistically the prediction of fish catch of the Chilika by help of stochastic models like Structural Time Series Model (STSM) and ARIMAX (Auto Regressive Integrated Moving Average with explanatory variables). Iglesias et al [22], Olden et al [23], Quetglas et al, [24], Wen et al [25] have applied neural computing models like Catch per unit effort (CPUE), Artificial Neural network (ANN), self-organizing map (SOM), multilayer perceptron (MLP) etc. for analysis and prediction for aqua catch in the Chilika which has turned up as active research for aquatic biodiversity.…”

Section: Review Of Literaturementioning

confidence: 99%

Prediction by Soft Computing, Planning, and Strategy Building of Aquatic Catch: Chilika Lagoon, Odisha, India

Mishra

Ojha

2021

ARRB

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Introduction: The Chilika lagoon in south Odisha, India was ecologically degraded from 1985 onwards by reduction of its aquatic (fish + prawn + shrimp) catches along with reduction in salinity, hydraulic regime, water exchange, aquatic weeds invasion, and sediment influx. The aquatic catch was 8669MT in year 1985-1986 gradually reduced to 1274MT during 1995-1996 from Odisha Fisheries Dept. records which resulted in poor economic condition of ≈0,2million fishermen and they migrated to adopt other livelihood. One direct tidal inlet dredged (Sipakuda) and Naraj barrage in the apex of South Mahanadi Delta were the major hydraulic interventions made to regain hydraulic regime. After the hydraulic interventions, the eco system restored, and the aquatic catch surged but it was insufficient to livelihood sustenance for the fishermen community of the Chilika, so that they are forced for alternate occupation and migration. Methodologies: Fish catch data collected for 30 years and soft computing models linear regression, Multi Linear Perception (ANN), SMOorg (SVM) and the Random Forest algorithms (Weka Software) are used to predict the fish catch data of the lagoon for coming decade from 2020 to 2030. The effects of major hydraulic interventions are analyzed and the soft computing method of the fish and shrimp catch prediction of the Chilika has been attempted for the first time except some statistical approaches. Results: The Random Forest is found to be the preferred algorithm followed by the MLP model. The amount of catch remained around 12-13TMT if the variables and the present status of the lagoon is maintained. The combined effect of the Sipakuda Tidal inlet and the effective operation of the Naraj barrage have maintained the sustainable aqua catch. The present study shall be an immense help for the lake users and policy makers to augment aquatic catch, and alternate livelihood fishers community of the Chilika lagoon.

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Forecasting tourism demand using search query data: A hybrid modelling approach

Cited by 46 publications

References 61 publications

A Study On The Influencing Factors Of Tourism Demand From Mainland China To Hong Kong

A Study On The Influencing Factors Of Tourism Demand From Mainland China To Hong Kong

Forecasting tourist arrivals: Google Trends meets mixed-frequency data

Prediction by Soft Computing, Planning, and Strategy Building of Aquatic Catch: Chilika Lagoon, Odisha, India

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