Combing Extended Kalman Filters and Support Vector Machines for Online Option Price Forecasting

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

Support Vector Machine Based Electricity Price Forecasting For Electricity Markets Utilising Projected Assessment of System Adequacy Data

In this paper we present an analysis of the results of a study into wholesale (spot) electricity price forecasting with Support Vector Machines (SVM) utilising past price and demand data and Projected Assessment of System Adequacy (PASA) data. The forecasting accuracy was evaluated using Australian National Electricity Market (NEM), New South Wales regional data over the year 2002. The inclusio...

Warrants Price Forecasting using Kernel Machine and EKF-ANN: a Comparative Study

Due to the six unreasonable assumptions companioned with the Black-Scholes options pricing model (BSM), which often make the miss-pricing result because of the difference of market convention in practical. This study try to combine the BSM and extended Kalman filters-based artificial neural networks (EKF-ANN) to deal with the limitation of consideration of the influences from many unexpected re...

Identification and Adaptive Position and Speed Control of Permanent Magnet DC Motor with Dead Zone Characteristics Based on Support Vector Machines

In this paper a new type of neural networks known as Least Squares Support Vector Machines which gained a huge fame during the recent years for identification of nonlinear systems has been used to identify DC motor with nonlinear dead zone characteristics. The identified system after linearization in each time span, in an online manner provide the model data for Model Predictive Controller of p...

A New Method for Crude Oil Price Forecasting Based on Support Vector Machines

This paper proposes a new method for crude oil price forecasting based on support vector machine (SVM). The procedure of developing a support vector machine model for time series forecasting involves data sampling, sample preprocessing, training & learning and out-of-sample forecasting. To evaluate the forecasting ability of SVM, we compare its performance with those of ARIMA and BPNN. The expe...