Forecasting Tourist Arrivals via Random Forest and Long Short-term Memory

Peng, Lu; Wang, Lin; Ai, Xue-Yi; Zeng, Yu‐Rong

doi:10.1007/s12559-020-09747-z

Cited by 42 publications

(15 citation statements)

References 44 publications

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“…Consider Carrier -B, a budget airline company that seeks to enhance its profitability and competitive edge via enhanced customer experience and lean operations. To achieve this, Carrier -B can use a DL model/algorithm (i.e., produce quadrant) to compute or determine the optimal (most profitable) routes (Peng et al, 2021) that it can travel to/from and apply DL to provide rich, tailored customer experiences to its individual travelers (e.g., customised travel packages, ticket prices, itineraries, recommendations, etc.). For example, recommending the Louvre or Grand Palais (Paris) travel package to an avid art lover.…”

Section: Discussionmentioning

confidence: 99%

Deep learning in hospitality and tourism: a research framework agenda for future research

Essien

Chukwukelu²

2022

IJCHM

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Purpose This study aims to provide a systematic review of the existing literature on the applications of deep learning (DL) in hospitality, tourism and travel as well as an agenda for future research. Design/methodology/approach Covering a five-year time span (2017–2021), this study systematically reviews journal articles archived in four academic databases: Emerald Insight, Springer, Wiley Online Library and ScienceDirect. All 159 articles reviewed were characterised using six attributes: publisher, year of publication, country studied, type of value created, application area and future suggestions (and/or limitations). Findings Five application areas and six challenge areas are identified, which characterise the application of DL in hospitality, tourism and travel. In addition, it is observed that DL is mainly used to develop novel models that are creating business value by forecasting (or projecting) some parameter(s) and promoting better offerings to tourists. Research limitations/implications Although a few prior papers have provided a literature review of artificial intelligence in tourism and hospitality, none have drilled-down to the specific area of DL applications within the context of hospitality, tourism and travel. Originality/value To the best of the authors’ knowledge, this paper represents the first theoretical review of academic research on DL applications in hospitality, tourism and travel. An integrated framework is proposed to expose future research trajectories wherein scholars can contribute significant value. The exploration of the DL literature has significant implications for industry and practice, given that this, as far as the authors know, is the first systematic review of existing literature in this research area.

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Section: Discussionmentioning

confidence: 99%

Deep learning in hospitality and tourism: a research framework agenda for future research

Essien

Chukwukelu²

2022

IJCHM

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“…Specifically, it selects a bootstrap sample from the training set, which is selected randomly with replacement, and then obtains the optimal split point to split the node into two subtrees by minimizing mean squared error (MSE), which is called growing a random forest tree, T m . After creation of M trees, the final output of random forest is defined as (Huang and Liu 2019, Peng et al 2021, Yoon 2021):…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Nowcasting of Lumber Futures Price with Google Trends Index Using Machine Learning and Deep Learning Models

He¹,

Li²,

Via³

et al. 2022

Forest Products Journal

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Firms engaged in producing, processing, marketing, or using lumber and lumber products always invest in futures markets to reduce the risk of lumber price volatility. The accurate prediction of real-time prices can help companies and investors hedge risks and make correct market decisions. This paper explores whether Internet browsing habits can accurately nowcast the lumber futures price. The predictors are Google Trends index data related to lumber prices. This study offers a fresh perspective on nowcasting the lumber price accurately. The novel outlook of employing both machine learning and deep learning methods shows that despite the high predictive power of both the methods, on average, deep learning models can better capture trends and provide more accurate predictions than machine learning models. The artificial neural network model is the most competitive, followed by the recurrent neural network model.

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“…The SARFIMA model has also been used by Mostafaei et al [9] to predict Iran's oil supply. In recent years, Peng et al [10] developed the new hybrid random forest-LSTM model to forecast tourist arrival data and justified by the Beijing city and Jiuzhaigou valley data that this hybrid approach outperforms. Waciko et al [11] used the Thief-MLP hybrid approach to forecast short-term tourists' arrival to Bali-Indonesia.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid SARFIMA-ANN Model for Tourism Forecasting

Saba¹,

Shahzad²,

Iqbal³

et al. 2022

Computers, Materials &Amp; Continua

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Many countries developed and increased greenery in their country sights to attract international tourists. This planning is now significantly contributing to their economy. The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment; it is only possible if an upcoming number of tourists' arrivals are accurately predicted. But accurate prediction is not easy as empirical evidence shows that the tourists' arrival data often contains linear, nonlinear, and seasonal patterns. The traditional model, like the seasonal autoregressive fractional integrated moving average (SARFIMA), handles seasonal trends with seasonality. In contrast, the artificial neural network (ANN) model deals better with nonlinear time series. To get a better forecasting result, this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN model. Then, we have used the proposed model to predict the tourists' arrival in New Zealand, Australia, and London. Empirical results showed that the proposed hybrid model outperforms in predicting tourists' arrival compared to the traditional SARFIMA and ANN models. Moreover, these results can be generalized to predict tourists' arrival in any country or region with a complicated data pattern.

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Forecasting Tourist Arrivals via Random Forest and Long Short-term Memory

Cited by 42 publications

References 44 publications

Deep learning in hospitality and tourism: a research framework agenda for future research

Deep learning in hospitality and tourism: a research framework agenda for future research

Nowcasting of Lumber Futures Price with Google Trends Index Using Machine Learning and Deep Learning Models

A New Hybrid SARFIMA-ANN Model for Tourism Forecasting

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