Comparison of Regression, Support Vector Regression (SVR), and SVR-Particle Swarm Optimization (PSO) for Rainfall Forecasting

Tehran Stock Price Modeling and Forecasting Using Support Vector Regression (SVR) and Its Comparison with the Classic Model ARIMA

The Model of Rainfall Forecasting by Support Vector Regression Based on Particle Swarm Optimization Algorithms

Accurate forecasting of rainfall has been one of the most important issues in hydrological research. In this paper, a novel neural network technique, support vector regression (SVR), to monthly rainfall forecasting. The aim of this study is to examine the feasibility of SVR in monthly rainfall forecasting by comparing it with back–propagation neural networks (BPNN) and the autoregressive integr...

PREDICTION OF EARTHQUAKE INDUCED DISPLACEMENTS OF SLOPES USING HYBRID SUPPORT VECTOR REGRESSION WITH PARTICLE SWARM OPTIMIZATION

Displacements induced by earthquake can be very large and result in severe damage to earth and earth supported structures including embankment dams, road embankments, excavations and retaining walls. It is important, therefore, to be able to predict such displacements. In this paper, a new approach to prediction of earthquake induced displacements of slopes (EIDS) using hybrid support vector re...

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

Hybridizing chaotic evolutionary algorithms with support vector regression (SVR) to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS) algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the se...

tehran stock price modeling and forecasting using support vector regression (svr) and its comparison with the classic model arima

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