SVM Approach for Forecasting International Tourism Arrival In East Java

Forecasting International Tourism Demand- an Empirical Case in Taiwan

Tourism, one of the biggest industries in many countries, has been considered a complexly integrated and self-contained economic activity.As key determinants of thetourism demand are not fully identified to some extent different forecasting models vary in thelevel of accuracy. By comparing the performance of diverse forecasting models,including the linear regression, autoregressive integrated m...

EC-SVM approach for real-time hydrologic forecasting

This study demonstrates a combined application of chaos theory and support vector machine (SVM) in the analysis of chaotic time series with a very large sample data record. A large data record is often required and causes computational difficulty. The decomposition method is used in this study to circumvent this difficulty. The various parameters inherent in chaos technique and SVM are optimise...

Hybridizing Gmdh and Least Squares Svm Support Vector Machine for Forecasting Tourism Demand

In this paper, we proposed a novel hybrid group method of data handling least squares support vector machine (GLSSVM) algorithm, which combines the theory a group method of data handling (GMDH) with the least squares support vector machine (LSSVM). With the GMDH is used to determine the inputs of LSSVM method and the LSSVM model which works as time series forecasting. The aim of this study is t...

Volatility in International Tourism Demand for Small Island Tourism Economies

Volatility in monthly international tourist arrivals is defined as the squared deviation from mean monthly international tourist arrivals. Consequently, volatility is directly related to the standard deviation, which is a common measure of financial risk. Fluctuating variations, or conditional volatility, in international monthly tourist arrivals are typically associated with unanticipated even...

Analyzing and Forecasting Tourism Demand: A Rough Sets Approach