Matrix computations (3rd ed.)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
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
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
IEEE Transactions on Information Technology in Biomedicine
A fuzzy varying coefficient model and its estimation
Computers & Mathematics with Applications
Generalized eigenvalue proximal support vector regressor
Expert Systems with Applications: An International Journal
A D-GMDH model for time series forecasting
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Asset portfolio optimization using support vector machines and real-coded genetic algorithm
Journal of Global Optimization
Save the best for last? The treatment of dominant predictors in financial forecasting
Expert Systems with Applications: An International Journal
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Hi-index | 12.06 |
In this paper, we propose a novel approach, termed as regularized least squares fuzzy support vector regression, to handle financial time series forecasting. Two key problems in financial time series forecasting are noise and non-stationarity. Here, we assign a higher membership value to data samples that contain more relevant information, where relevance is related to recency in time. The approach requires only a single matrix inversion. For the linear case, the matrix order depends only on the dimension in which the data samples lie, and is independent of the number of samples. The efficacy of the proposed algorithm is demonstrated on financial datasets available in the public domain.