Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines:Evidence from Taiwan

Abstract

In the past few decades, international tourism has grown rapidly and has become a very interesting topic in tourism research. Taiwan, acting as a citizen in the global community, improved traveling facilities, and governments' strong promotion has drawn more and more visitors to visit Taiwan. This study tries to build the forecasting model of visitors to Taiwan using three commonly adopted ARIMA, artificial neural networks (ANNs), and multivariate adaptive regression splines (MARS). In order to evaluate the appropriateness of the proposed modeling approaches, the dataset of monthly visitors to Taiwan was used as the illustrative example. Analytic results demonstrated that ARIMA outperformed ANNs and MARS approaches in terms of RMSE, MAD, and MAPE and provided effective alternatives for forecasting tourism demand.

Data Pre-Processing for Neural Network-Based Forecasting: Does It Really Matter?

“…Many authors have acknowledged the importance of applying new approaches to tourism demand forecasting in order to improve the accuracy of the methods of analysis (Song, Li 2008). The availability of more advanced forecasting techniques has led to a growing interest Artificial Intelligence (AI) models (Yu, Schwartz 2006;Goh et al 2008;Lin et al 2011;Chen 2011;Celotto et al 2012;Wu et al 2012;Cang, Yu 2014) to the detriment of time series models (Chu 2008(Chu , 2011Assaf et al 2011) and causal econometric models (Page et al 2012). Some of the new AI based techniques are fuzzy time series models (Tsaur, Kuo 2011), genetic algorithms (Hadavandi et al 2011), expert systems (Shahrabi et al 2013;Pai et al 2014) and Support Vector Machines (SVMs) (Chen, Wang 2007;Hong et al 2011).…”

Predicting Foreign Tourists for the Tourism Industry Using Soft Computing-Based Grey–Markov Models

“…However, time series data for tourism often have temporal fluctuation and trend changes, making precise predictions challenging. Over or under estimation of foreign tourist numbers could lead to inappropriate governmental investment in tourist infrastructures [41]. This study proposed a novel grey residual modification model incorporating soft computing, including SLP, FLN, and GA, into the Grey-Markov model.…”

Oleotourism as a Sustainable Product: An Analysis of Its Demand in the South of Spain (Andalusia)

“…In addition, several variants of the ARIMA modeling approach have also been applied in most of the post-2000 studies that used forecast techniques with time series [47]. In this regard, several previous studies have shown that the ARIMA model and its variants obtain good results in the forecast of tourist demand and, in most cases, exceed other time series methods, such as the periodic autoregressive model [48,49], the moving-average and exponential smoothing models [50] and the multivariate adaptive regression splines [51].…”