Tourism demand forecasting using tourist-generated online review data

The need for accurate tourism demand forecasting is widely recognized. The unreliability of traditional methods makes tourism demand forecasting still challenging. Using deep learning approaches, this study aims to adapt Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit networks (GRU), which are straightforward and efficient, to improve Taiwan’s tourism demand forecasting. The networks are able to seize the dependence of visitor arrival time series data. The Adam optimization algorithm with adaptive learning rate is used to optimize the basic setup of the models. The results show that the proposed models outperform previous studies undertaken during the Severe Acute Respiratory Syndrome (SARS) events of 2002–2003. This article also examines the effects of the current COVID-19 outbreak to tourist arrivals to Taiwan. The results show that the use of the LSTM network and its variants can perform satisfactorily for tourism demand forecasting.

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Pooling in Tourism Demand Forecasting

Journal of Travel Research ◽

10.1177/0047287518800390 ◽

2018 ◽

Vol 58 (7) ◽

pp. 1161-1174 ◽

Cited By ~ 7

Author(s):

Wen Long ◽

Chang Liu ◽

Haiyan Song

Keyword(s):

Panel Data ◽

Fixed Effects ◽

Demand Forecasting ◽

Spillover Effects ◽

Ordinary Least Squares ◽

Spatial Effects ◽

Tourism Demand ◽

Forecasting Accuracy ◽

Demand Models ◽

Forecasting Performance

This study investigates whether pooling can improve the forecasting performance of tourism demand models. The short-term domestic tourism demand forecasts for 341 cities in China using panel data (pooled) models are compared with individual ordinary least squares (OLS) and naïve benchmark models. The pooled OLS model demonstrates much worse forecasting performance than the other models. This indicates the huge heterogeneity of tourism across cities in China. A marked improvement with the inclusion of fixed effects suggests that destination features that stay the same or vary very little over time can explain most of the heterogeneity. Adding spatial effects to the panel data models also increases forecasting accuracy, although the improvement is small. The spatial distribution of spillover effects is drawn on a map and a spatial pattern is recognized. Finally, when both spatial and temporal effects are taken into account, pooling improves forecasting performance.

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Forecasting tomorrow’s tourist

Worldwide Hospitality and Tourism Themes ◽

10.1108/whatt-09-2016-0046 ◽

2016 ◽

Vol 8 (6) ◽

pp. 643-653 ◽

Cited By ~ 8

Author(s):

Sérgio Moro ◽

Paulo Rita

Keyword(s):

Literature Review ◽

Design Methodology ◽

Scientific Literature ◽

Recent Literature ◽

Demand Forecasting ◽

Tourism Demand ◽

Content Type ◽

Current Trends ◽

Modeling Techniques ◽

Excellent Source

Purpose This study aims to present a very recent literature review on tourism demand forecasting based on 50 relevant articles published between 2013 and June 2016. Design/methodology/approach For searching the literature, the 50 most relevant articles according to Google Scholar ranking were selected and collected. Then, each of the articles were scrutinized according to three main dimensions: the method or technique used for analyzing data; the location of the study; and the covered timeframe. Findings The most widely used modeling technique continues to be time series, confirming a trend identified prior to 2011. Nevertheless, artificial intelligence techniques, and most notably neural networks, are clearly becoming more used in recent years for tourism forecasting. This is a relevant subject for journals related to other social sciences, such as Economics, and also tourism data constitute an excellent source for developing novel modeling techniques. Originality/value The present literature review offers recent insights on tourism forecasting scientific literature, providing evidences on current trends and revealing interesting research gaps.

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Tourism Demand Forecasting Using Nonadditive Forecast Combinations

Journal of Hospitality & Tourism Research ◽

10.1177/10963480211047857 ◽

2021 ◽

pp. 109634802110478

Author(s):

Yi-Chung Hu ◽

Geng Wu ◽

Peng Jiang

Keyword(s):

Prediction Models ◽

Demand Forecasting ◽

Combination Method ◽

Fuzzy Integral ◽

Tourism Demand ◽

Grey Prediction ◽

Investment Planning ◽

Single Model ◽

Combining Forecasts ◽

Forecast Combinations

Accurately forecasting the demand for tourism can help governments formulate industrial policies and guide the business sector in investment planning. Combining forecasts can improve the accuracy of forecasting the demand for tourism, but limited work has been devoted to developing such combinations. This article addresses two significant issues in this context. First, the linear combination is the commonly used method of combining tourism forecasts. However, additive techniques unreasonably ignore interactions among the inputs. Second, the available data often do not adhere to specific statistical assumptions. Grey prediction has thus drawn attention because it does not require that the data follow any statistical distribution. This study proposes a nonadditive combination method by using the fuzzy integral to integrate single-model forecasts obtained from individual grey prediction models. Using China and Taiwan tourism demand as empirical cases, the results show that the proposed method outperforms the other combined methods considered here.

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Forecasting Tourism Demand with an Improved Mixed Data Sampling Model

Journal of Travel Research ◽

10.1177/0047287520906220 ◽

2020 ◽

Vol 60 (2) ◽

pp. 336-353 ◽

Cited By ~ 2

Author(s):

Long Wen ◽

Chang Liu ◽

Haiyan Song ◽

Han Liu

Keyword(s):

Factor Model ◽

Moving Average ◽

Demand Forecasting ◽

Mainland China ◽

Mixed Data ◽

Dynamic Factor ◽

Data Sampling ◽

Tourism Demand ◽

Composite Indices ◽

Mixed Data Sampling

Search query data reflect users’ intentions, preferences and interests. The interest in using such data to forecast tourism demand has increased in recent years. The mixed data sampling (MIDAS) method is often used in such forecasting, but is not effective when moving average (MA) dynamics are involved. To investigate the relevance of the MA components in MIDAS models to tourism demand forecasting, an improved MIDAS model that integrates MIDAS and the seasonal autoregressive integrated moving average process is proposed. Its performance is tested by forecasting monthly tourist arrivals in Hong Kong from mainland China with daily composite indices constructed from a large number of search queries using the generalized dynamic factor model. The forecasting results suggest that this new model significantly outperforms the benchmark model. In addition, comparing the forecasts and nowcasts shows that the latter generally outperforms the former.

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Quantile Dependence in Tourism Demand Time Series: Evidence in the Southern Italy Market

Sustainability ◽

10.3390/su12083243 ◽

2020 ◽

Vol 12 (8) ◽

pp. 3243

Author(s):

Giovanni De Luca ◽

Monica Rosciano

Keyword(s):

Demand Forecasting ◽

Tourism Industry ◽

Tourism Demand ◽

Campania Region ◽

Travel And Tourism ◽

Tourist Destinations ◽

High Quantiles ◽

International Tourist ◽

Long Time ◽

Hotel Accommodation

Travel and tourism is an important economic activity in most countries around the world. In 2018, international tourist arrivals grew 5% to reach the 1.4 billion mark and at the same time export earnings generated by tourism have grown to USD 1.7 trillion. The rapid growth of the tourism industry has globally attracted the interest of researchers for a long time. The literature has tried to model tourism demand to analyze the effects of different factors and predict the future behavior of the demand. Forecasting of tourism demand is crucial not only for academia but for tourism industries too, especially in line with the principles of sustainable tourism. The hospitality branch is an important part of the tourism industry and accurate passenger flow forecasting is a key link in the governance of the resources of a destination or in revenue management systems. In this context, the paper studies the interdependence of tourism demand in one of the main Italian tourist destinations, the Campania region, using a quantile-on-quantile approach between overall and specific tourism demand. Data are represented by monthly arrivals and nights spent by residents and non-residents in hotels and complementary accommodations from January 2008 to December 2018. The results of the analysis show that the hotel-accommodation component of the tourism demand appears to be more vulnerable than extra-hotel accommodation component to the fluctuations of the overall tourism demand and this feature is more evident for the arrivals than for nights spent. Moreover, the dependence on high quantiles suggests strategy of diversification or market segmentation to avoid overtourism phenomena and/or carrying capacity problems. Conversely, dependence on low quantiles suggests the use of push strategies to stimulate tourism demand. Finally, the results suggest that it could be very useful if the stakeholders of the tourism sector in Campania focused their attention on the collaboration theory.

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