Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) has been successfully used to solve nonlinear regression and times series problems. However, parameters determination for a SVR model is competent to the forecasting...

In the car-rental industry, it is generally assumed that a 1% increase in price contributes an 8% profit increment. Therefore, pricing is of priority concern in revenue management. Accurately forecasting monthly revenue per unit (RPU) (per car) has received increasing attention owing to its...

Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) has been successfully used to solve nonlinear regression and times series problems. However, parameters determination for a SVR model is competent to the forecasting...

Monthly revenue per unit (RPU), measured by the total monthly revenue divided by the total monthly number of cars in the fleet, is one of the most popular profitability assessments in the car-rental industry. Because pricing decision is influenced by profit a lot, accurate forecast of RPU is...

To minimize potential loss of life and property caused by rainfall during typhoon seasons, precise rainfall forecasts have been one of the key subjects in hydrological research. However, rainfall forecast is made difficult by some very complicated and unforeseen physical factors associated with...

Forecasting and monitoring of rainfall values are increasingly important for decreasing economic loss caused by flash floods. Based on statistical learning theory, support vector regression (SVR) has been used to deal with forecasting problems. Performing structural risk minimization rather than...

In contemporary finance, derivatives such as futures and options have exerted increasingly prominent impact not only on risk management activities but also on price speculative activities. As a result of the high-leverage characteristic involved in derivative tradings, investors can gain...

Accurate tourist demand forecasting systems are essential in tourism planning, particularly in tourism-based countries. Artificial neural networks are attracting attention to forecast tourist arrivals due to their general nonlinear mapping capabilities. Unlike most conventional neural network...

Support vector machines (SVMs) have been successfully applied to solve nonlinear regression and times series problems. However, the application of SVMs for tourist forecasting has not been widely explored. Furthermore, most SVM models are applied for solving univariate forecasting problems....

Support vector machines (SVMs) have been successfully employed to solve non-linear regression and time series problems. However, SVMs have rarely been applied to forecasting software reliability. This investigation elucidates the feasibility of the use of SVMs to forecast software reliability....