IEEE Transactions on Information Theory
Robust Linear and Support Vector Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Generalized Representer Theorem
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Neural Computing and Applications
A prediction algorithm for time series based on adaptive model selection
Expert Systems with Applications: An International Journal
Online prediction of time series data with kernels
IEEE Transactions on Signal Processing
Support vector machines of interval-based features for time series classification
Knowledge-Based Systems
Time series forecasting by a seasonal support vector regression model
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
Support vector machine techniques for nonlinear equalization
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
Prediction of chaotic time series based on the recurrent predictor neural network
IEEE Transactions on Signal Processing
Predicting sun spots using a layered perceptron neural network
IEEE Transactions on Neural Networks
Robust support vector regression networks for function approximation with outliers
IEEE Transactions on Neural Networks
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
IEEE Transactions on Neural Networks
An Information Theoretic Approach of Designing Sparse Kernel Adaptive Filters
IEEE Transactions on Neural Networks
Shape-Based clustering for time series data
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
An information theoretic sparse kernel algorithm for online learning
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Kernel based methods have been widely applied for signal analysis and processing. In this paper, we propose a sparse kernel based algorithm for online time series prediction. In classical kernel methods, the kernel function number is very large which makes them of a high computational cost and only applicable for off-line or batch learning. In online learning settings, the learning system is updated when each training sample is obtained and it requires a higher computational speed. To make the kernel methods suitable for online learning, we propose a sparsification method based on the Hessian matrix of the system loss function to continuously examine the significance of the new training sample in order to select a sparse dictionary (support vector set). The Hessian matrix is equivalent to the correlation matrix of sample inputs in the kernel weight updating using the recursive least square (RLS) algorithm. This makes the algorithm able to be easily implemented with an affordable computational cost for real-time applications. Experimental results show the ability of the proposed algorithm for both real-world and artificial time series data forecasting and prediction.