Financial Forecasting With Multivariate Adaptive Regression Splines and Queen Genetic Algorithm-Support Vector Regression

Multivariate Adaptive Regression Splines

Tourism Demand Forecasting: Econometric Model based on Multivariate Adaptive Regression Splines, Artificial Neural Network and Support Vector Regression

This paper develops tourism demand econometric models based on the monthly data of tourists to Taiwan and adopts Multivariate Adaptive Regression Splines (MARS), Artificial Neural Network (ANN) and Support Vector Regression (SVR), MARS, ANN and SVR to develop forecast models and compare the forecast results. The results showed that SVR model is the optimal model, with a mean error rate of 3.61%...

G/SPLINES: A Hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) Algorithm with Holland's Genetic Algorithm

G/SPLINES ate a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least-squares computations, and allows significantly larger problems to be considered.

Discussion: Multivariate Adaptive Regression Splines

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Survival Analysis with Multivariate adaptive Regression Splines

Multivariate adaptive regression splines (MARS) are a useful tool to identify linear and nonlinear effects and interactions between two covariates. In this dissertation a new proposal to model survival type data with MARS is introduced. Martingale and deviance residuals of a Cox PH model are used as response in a common MARS approach to model functional forms of covariate effects as well as pos...