Thailand tourism forecasting based on a hybrid of discrete wavelet decomposition and NARX neural network

Abstract

Purpose – Accurate forecast of tourist arrivals is crucial for Thailand since the tourism industry is a major economic factor of the country. However, a nonstationarity, normally consisted in nonlinear tourism time series can seriously ruin the forecasting computation. The purpose of this paper is to propose a hybrid forecasting method, namely discrete wavelet decomposition (DWD)-NARX, which combines DWD and the nonlinear autoregressive neural network with exogenous input (NARX) to cope with such nonstationarity, as a consequence, improve the effectiveness of the demand-side management activities. Design/methodology/approach – According to DWD-NARX, wavelet decomposition is executed for efficiently extracting the hidden significant, temporal features contained in the nonstationary time series. Then, each extracted feature set at a particular resolution level along with a relative price as an exogenous input factor are fed into NARX for further forecasting. Finally, the forecasting results are reconstructed. Forecasting performance measures rely on mean absolute percentage error, mean absolute error as well as mean square error. Model overfitting avoidance is also considered. Findings – The results indicate the superiority of the DWD-NARX over other efficient related neural forecasters in the cases of high forecasting performance rate as well as competently coping with model overfitting. Research limitations/implications – The scope of this study is confined to Thailand tourist arrivals forecast based on short-term projection. To resolve such limitations, future research should aim to apply the generalization capability of DWD-NARX on other domains of managerial time series forecast under long-term projection environment. However, the exogenous input factor is to be empirically revised on domain-by-domain basis. Originality/value – Few works have been implemented either to handle the nonstationarity, consisted in nonlinear, unpredictable time series, or to achieve great success on finding an appropriate and effective exogenous forecasting input. This study applies DWD to attain efficient feature extraction; then, utilizes the competent forecaster, NARX. This would comprehensively and specifically deal with the nonstationarity difficulties at once. In addition, this study finds the effectiveness of simply using a relative price, generated based on six top-ranked original tourist countries as an exogenous forecasting input.

A new decomposition ensemble approach for tourism demand forecasting: Evidence from major source countries in Asia‐Pacific region

“…In recent years, many researchers have been exploring the different market impact factors driving the volatility of the tourism market at different time frequencies (Goh, 2012; Pizam & Fleischer, 2002; Wang, 2009; Wu & Wu, 2019). Accordingly, several authors have paid more attention to the application of data decomposition methods for analyzing tourism demand from the perspectives of time frequencies, such as spectral analysis (Coshall, 2000; Kožić, 2014), singular spectrum analysis (SSA; Beneki et al, 2012; Hassani et al, 2015), Fourier decomposition (Apergis et al, 2017), wavelet decomposition (Kummong & Supratid, 2016) and the empirical mode decomposition (EMD) family (Chen et al, 2012; Li et al, 2016; Li & Law, 2019; Zhang, Wu, et al, 2017a). Table 1 summarizes the related literature that incorporated data decomposition methods to analyze and forecast tourism demand.…”

Long-term forecasting system using wavelet – nonlinear autoregressive neural network conjunction model

“…In dynamic neural networks, the output enters into the network input with a delay; in contrast, the output in the static networks is not used as input. One of powerful dynamic nonlinear forecasting systems, a NAR (Chen et al , 1990; Narendra and Parthasarathy, 1990) has been extensively used in various nonlinear, complex information systems with an aid of some related exogenous input (NARX) (Tijani et al , 2014; Kummong and Supratid, 2016). The work (Chang et al , 2014) also pointed the outperformance of NAR over BPNN a traditional RNN (Elman, 1990) in a real-time long-term multi-step water level forecast.…”

“…Therefore, DWT has been used in several domains to decompose original data into wavelet components at different frequency resolution levels for further machine-learning forecasts. Such combination between DWT and machine-learning model for forecasting applications included daily precipitation forecast (Kisi and Cimen, 2012), monthly tourism forecast (Kummong and Supratid, 2016) using DWT-SVR and DWT-NARX, respectively, and drought and river flow long-term multi-step forecasts (Belayneh et al , 2014; Badrzadeh et al , 2013) based on DWT-BPNN, DWT-SVR, DWT-ANFIS, where ANFIS refers to adaptive neuro-fuzzy inference system (Jang, 1993). In addition, the work (Jaipuria and Mahapatra, 2019) combined DWT and multi-gene genetic programming (MGGP), namely, DWT-MGGP for demand forecasts, with regard to inventory control under uncertain environment.…”

Machine learning facilitated business intelligence (Part I)

“…It can solve any complex non-linear problem more accurately which is difficult using classical statistical techniques (Kumar et al, 1995;Tkáč and Verner, 2016;Tu, 1996). The FNN range of applications is numerous and some areas include regression estimation (Chung et al, 2017;Deng et al, 2019;Kummong and Supratid, 2016;Teo et al, 2015), image processing (Dong et al, 2016;Mohamed Shakeel et al, 2019), image segmentation (Chen et al, 2018), video processing (Babaee et al, 2018), speech recognition (Abdel-Hamid et al, 2014), text classification (Kastrati et al, 2019;Zaghloul et al, 2009), face classification and recognition (Yin and Liu, 2018), human action recognition (Ijjina and Chalavadi, 2016), risk analysis (Nasir et al, 2019) and many others. Business intelligence makes use of data analytics techniques to generate useful information from high-dimensional data that may support making better informed decisions.…”