Forecasting International Tourist Arrivals in Malaysia Using Sarima and Holt-Winters Model

Lip, Norliana Mohd; Jumery, Nur Shafiqah; Termizi, Fatin Amira Ahmad; Mulyadi, Nurul Atiqa; Anuar, Nurina; Ithnin, Hanafi

doi:10.35631/jthem.518004

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…The pandemic has introduced a great deal of uncertainty in all aspects of life. Factors such as the financial speed of policy ARIMA-HOLD OUT For International 1.45% [82] ANN CGK-JOG 1.29% [25] MLP Neural Net (12-2-1) 2.80% [116] SARIMA-SVR Passenger Traffic Volume 2.31% [67] Regression + SVM Model 5.07% [62] Regression prediction 2.30% [33] LSTM 4.00% [72] Holt-Winters SARIMA -Singapore 2.69% Tourism [9,25,31,41,43,65,77,93,112] Revenue Management [61,87] Infrastructure Management [10,13,42,96,100] changes in all sectors impact forecasting passenger demand directly or indirectly. Few studies have found that aviation recovered after the pandemic [52].…”

Section: Forecasting Challenges With Pandemicmentioning

confidence: 99%

Systematic review of passenger demand forecasting in aviation industry

Zachariah¹,

Sharma

Kumar

2023

Multimed Tools Appl

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Forecasting aviation demand is a significant challenge in the airline industry. The design of commercial aviation networks heavily relies on reliable travel demand predictions. It enables the aviation industry to plan ahead of time, evaluate whether an existing strategy needs to be revised, and prepare for new demands and challenges. This study examines recently published aviation demand studies and evaluates them in terms of the various forecasting techniques used, as well as the advantages and disadvantages of each. This study investigates numerous forecasting techniques for passenger demand, emphasizing the multiple factors that influence aviation demand. It examined the benefits and drawbacks of various models ranging from econometric to statistical, machine learning to deep neural networks, and the most recent hybrid models. This paper discusses multiple application areas where passenger demand forecasting is used effectively. In addition to the benefits, the challenges and potential future scope of passenger demand forecasting were discussed. This study will be helpful to future aviation researchers while also inspiring young researchers to pursue careers in this industry.

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Section: Forecasting Challenges With Pandemicmentioning

confidence: 99%

Systematic review of passenger demand forecasting in aviation industry

Zachariah¹,

Sharma

Kumar

2023

Multimed Tools Appl

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“…Usually, seasonal autoregressive integrated moving average or SARIMA model is a standard method used for modeling the number of tourist arrivals in many researches. Recent research using the SARIMA model includes the study by Hwandee and Phumchusri, (2020) and Lip et al (2020). Nevertheless, to assess the impacts of interrupted incidents or prior known interventions on the number of tourist arrivals, the SARIMA with intervention model is often employed.…”

Section: Literature Reviewmentioning

confidence: 99%

The impact of insurgency in the deep south of Thailand on the arrival of Malaysian tourists to Betong district, Yala province using SARIMA with intervention model

2022

KJSS

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This paper investigated the impacts of the insurgency on the arrival of Malaysian tourists to the Betong district in Yala province by applying the seasonal autoregressive integrated moving average with the intervention model. The results revealed that ten incidents had a significant impact on the number of Malaysian tourist arrivals, such as the bombing of electricity pole that led to the highest decrease in tourist arrivals to approximately 17,413 persons in July 2015. Moreover, the forecasts of the SARIMA (1,1,1) (1,0,1) 12 with intervention model revealed that the number of Malaysians traveling to Betong in the first quarter of 2020 before COVID-19 pandemic were 24,193, 31,659 and 25,309 persons, respectively, which are close to the preliminary number of the international tourist arrivals to Yala.

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Predictive analytics of COVID-19 cases and tourist arrivals in ASEAN based on covid-19 cases

Velu

Ravi²,

Tabianan³

2022

Health Technol.

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Purpose Research into predictive analytics, which helps predict future values using historical data, is crucial. In order to foresee future instances of COVID-19, a method based on the Seasonal ARIMA (SARIMA) model is proposed here. Additionally, the suggested model is able to predict tourist arrivals in the tourism business by factoring in COVID-19 during the pandemic. In this paper, we present a model that uses time-series analysis to predict the impact of a pandemic event, in this case the spread of the Coronavirus pandemic (Covid-19). Methods The proposed approach outperformed the Autoregressive Integrated Moving Average (ARIMA) and Holt Winters models in all experiments for forecasting future values using COVID-19 and tourism datasets, with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), and root mean squared error (RMSE). The SARIMA model predicts COVID-19 and tourist arrivals with and without the COVID-19 pandemic with less than 5% MAPE error. Results The suggested method provides a dashboard that shows COVID-19 and tourism-related information to end users. The suggested tool can be deployed in the healthcare, tourism, and government sectors to monitor the number of COVID-19 cases and determine the correlation between COVID-19 cases and tourism. Conclusion Management in the tourism industries and stakeholders are expected to benefit from this study in making decisions about whether or not to keep funding a given tourism business. The datasets, codes, and all the experiments are available for further research, and details are included in the appendix.

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Forecasting International Tourist Arrivals in Malaysia Using Sarima and Holt-Winters Model

Cited by 5 publications

References 27 publications

Systematic review of passenger demand forecasting in aviation industry

Systematic review of passenger demand forecasting in aviation industry

The impact of insurgency in the deep south of Thailand on the arrival of Malaysian tourists to Betong district, Yala province using SARIMA with intervention model

Predictive analytics of COVID-19 cases and tourist arrivals in ASEAN based on covid-19 cases

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