Open Access

ARTICLE

A New Hybrid SARFIMA-ANN Model for Tourism Forecasting

Tanzila Saba¹, Mirza Naveed Shahzad^2,*, Sonia Iqbal^2,3, Amjad Rehman¹, Ibrahim Abunadi¹
1 Artificial Intelligence & Data Analytics Research Lab (AIDA), CCIS Prince Sultan University, Riyadh, 11586, Saudi Arabia
2 Department of Statistics, University of Gujrat, Gujrat, 50700, Pakistan
3 Department of Statistics, University of Sialkot, Sialkot, 51310, Pakistan
* Corresponding Author: Mirza Naveed Shahzad. Email:

Computers, Materials & Continua 2022, 71(3), 4785-4801. https://doi.org/10.32604/cmc.2022.022309

Received 03 August 2021; Accepted 29 October 2021; Issue published 14 January 2022

Abstract

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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Keywords

SARFIMA; hybrid model; tourists’ arrival forecasting; ANN

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