A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
ϵ-Descending Support Vector Machines for Financial Time Series Forecasting
Neural Processing Letters
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Support Vector Machine for Regression and Applications to Financial Forecasting
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
A hybrid genetic algorithm with pattern search for finding heavy atoms in protein crystals
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Bounds on Error Expectation for Support Vector Machines
Neural Computation
Expert Systems with Applications: An International Journal
Grey relational grade in local support vector regression for financial time series prediction
Expert Systems with Applications: An International Journal
Save the best for last? The treatment of dominant predictors in financial forecasting
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The complex model GMRVV"m-SVR has been adopted to predict financial time series with such characteristics as small sample size, poor information, non-stationary, high noise and non-linearity. In order to construct GMRVV"m-SVR, the m-root grey model with revised verge value (GMRVV"m) has been introduced and modified by support vector regression based on the calculation of the residual error sequence between predicted values and original data. Due to the recent data points providing more information than distant data points, more importance has been attached to the punishment parameter C of recent data points in support vector regression. Simultaneously, the parameter @? in @?-insensitive loss function has been determined according to smoothing overshooting. Pattern search (PS) algorithm has been carried out to tune free parameters. A real experimental result shows that the complex model can achieve comparative accurate prediction as well as smoothing overshooting in financial time series prediction.