Support Vector Regression in Forecasting

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

Monthly rainfall Forecasting using genetic programming and support vector machine

Rainfall and runoff estimation play a fundamental and effective role in the management and proper operation of the watershed, dams and reservoirs management, minimizing the damage caused by floods and droughts, and water resources management. The optimal performance of intelligent models has increased their use to predict various hydrological phenomena. Therefore, in this study, two intelligent...

Short Term Load Forecasting Using Empirical Mode Decomposition, Wavelet Transform and Support Vector Regression

The Short-term forecasting of electric load plays an important role in designing and operation of power systems. Due to the nature of the short-term electric load time series (nonlinear, non-constant, and non-seasonal), accurate prediction of the load is very challenging. In this article, a method for short-term daily and hourly load forecasting is proposed. In this method, in the first step, t...

Modeling and forecasting US presidential election using learning algorithms

The primary objective of this research is to obtain an accurate forecasting model for the US presidential election. To identify a reliable model, artificial neural networks (ANN) and support vector regression (SVR) models are compared based on some specified performance measures. Moreover, six independent variables such as GDP, unemployment rate, the president’s approval rate, and others are co...

Machine learning algorithms in air quality modeling

Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...

A Wavelet Support Vector Machine Combination Model for Daily Suspended Sediment Forecasting

Abstract In this study, wavelet support vector machine (WSWM) model is proposed for daily suspended sediment (SS) prediction. The WSVM model is achieved by combination of two methods; discrete wavelet analysis and support vector machine (SVM). The developed model was compared with single SVM. Daily discharge (Q) and SS data from Yadkin River at Yadkin College, NC station in the USA were used. I...