A Wavelet Support Vector Machine Combination Model for Singapore Tourist Arrival to Malaysia

Rafidah, Ali; Shabri, Ani; Nurulhuda, A.; Suhaila, Yacob

doi:10.1088/1757-899x/226/1/012077

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…This is because, in dealing with nonlinear systems that are complex, these methods are able to fit the expression of a function of a system that is unknown and the methods are effective too. However, this method cannot fully identify and extract the internal characteristics of complex nonlinear and non-stationary timeseries [8].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Empirical mode Decomposition with Wavelets Support Vector Machine in time Series Data

Mitec¹,

Rafidah²,

Shabri³

et al. 2020

IJITEE

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This paper mainly discussed on the forecast of Thailand tourist visiting Malaysia. This paper proposed a three-stage technique in which the empirical mode decomposition (EMD) is combined with wavelet methods and support vector machine model. We used the proposed technique, EMD_WSVM to forecast two ASEAN country tourism timeseries. Detail experiments are conducted for the proposed method, in which there is a comparison between the EMD_WSVM, WSVM and SVM methods. The proposed EMD_WSVM model is determined to be dominant to the other methods in predicting the number of tourist arrivals

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Section: Discussionmentioning

confidence: 99%

“…The WSVM model is an SVM model, which uses sub-time series components obtained using DWT on original data [7]. For WSVM model inputs, the original time series data are decomposed into a certain number of sub-time series components (Ds) [8].…”

Section: The Discrete Wavelet Transforms Methods (Wsvm)mentioning

confidence: 99%

Application of Empirical mode Decomposition with Wavelets Support Vector Machine in time Series Data

Mitec¹,

Rafidah²,

Shabri³

et al. 2020

IJITEE

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“…International tourism demand models use most frequently tourist arrivals/departures and expenditures/receipts as the dependent variables (Kulendran & Wong, 2005;Coshall, 2005;Rosselló, 2001;Tang, et al, 2015;Cankurt & Subasi, 2016;Rafidah, et al, 2017), while there also a few studies which measure the number of overnight stays such as these of Claveria & Torra (2014) and Constantino et al (2016). The most common explanatory variables used, are the real gross domestic product for approaching the tourist incomes, the consumer price index, the tourism cost of the destination country relative to the country of origin, the exchange rate, the living cost, as well as the price of the competing destination , Constantinino et al, 2016Song et al, 2011;Cankurt et al, 2015;Gunter, 2015;Zhu, et al, 2018;Assaf et al, 2019).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A Panel Data Model of International Tourism Demand for Greece

Mavrommati¹,

Pendaraki²,

Kontogeorgos³

et al. 2022

DETUROPE

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Tourism is an important industry which affects the profits of national economy. A strong tourism sector directly contributes to the national income of the country, combats unemployment and improves the balance of payments. Tourism demand is usually measured by the number of tourist visits from an origin country to a destination country, in terms of tourist nights spent in the destination country or in terms of tourist expenditures by visitors from an origin country to the destination country. The purpose of this study is to investigate the determinants of international tourism demand for Greece and to quantify their influences. Four econometric models have been developed with different combinations of countries, to estimate tourist inflow data from twenty-eight European and non-European countries, for the period 1996-2015. Various potential determinants are investigated, including gross domestic product, currency, the average per capita tourism expenditure and the marketing expenses to promote tourism industry. The empirical results indicate that the explanatory variables affect the tourism demand of Greece and play an important role in strategies that affect total cost, demand, and structure of the Greek tourism market.

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“…Ghalehkhondabi reviewed the demand forecasting methods within tourism passenger transportation [21], and the main methods include time series models [22][23][24][25], autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), and the seasonal ARIMA(SARIMA) [26][27][28], regression models [29][30][31], support vector machines [32][33][34], artificial neural networks (ANN) models [23,35,36].…”

Section: Passenger Flow Prediction Researchmentioning

confidence: 99%

Travel Characteristics Analysis and Passenger Flow Prediction of Intercity Shuttles in the Pearl River Delta on Holidays

et al. 2020

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As China’s urbanization process continues to accelerate, the demand for intercity residents’ transportation has increased dramatically. Holiday travel has different demand characteristics, causing serious shortage during peak periods. However, current research barely focuses on the passenger flow prediction along with travel characteristics of intercity shuttles. Accurately predicting passenger flow during the holidays helps to improve operational organization efficiency and residents’ satisfaction, and provides a basis for reasonable resource allocation by the management department. This paper analyzes the spatiotemporal characteristics of intercity shuttles passenger flow in the Pearl River Delta. Separate passenger flow prediction models on non-holiday and holiday are established using an improved genetic algorithm optimized back propagation neural network (IGA-BPNN) based on the characteristics of passenger flow, and the prediction models are validated based on panel data. The results of weekly flow show obvious holiday characteristics, and the hourly traffic flow of holidays is much larger than that of weekends and weekdays. There is a significant difference in the hourly flow between different holidays. The IGA-BPNN model used in this paper achieves lower prediction error relative to the benchmark BPNN approach (leads a two thirds reduction in MAPE, and an over 85% reduction in MSPE).

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A Wavelet Support Vector Machine Combination Model for Singapore Tourist Arrival to Malaysia

Cited by 6 publications

References 4 publications

Application of Empirical mode Decomposition with Wavelets Support Vector Machine in time Series Data

Application of Empirical mode Decomposition with Wavelets Support Vector Machine in time Series Data

A Panel Data Model of International Tourism Demand for Greece

Travel Characteristics Analysis and Passenger Flow Prediction of Intercity Shuttles in the Pearl River Delta on Holidays

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