Tanzila Saba1, Mirza Naveed Shahzad2,*, Sonia Iqbal2,3, Amjad Rehman1, Ibrahim Abunadi1
CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4785-4801, 2022, DOI:10.32604/cmc.2022.022309
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… More >
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