Application of critical support vector machine to time series prediction

This paper proposes a novel technique to approximate functions for time series and signal processing using the special type of neural network, called Critical Support Vector Machine (CSVM). CSVM is a combination of the Support Vector Machine, the Nearest Neighbor Algorithm and the Perceptron. The CSVM has been shown to be an effective method for classification problems. In this work, we generalize CSVM so that it can be used for the application of time series prediction. The experiment on the chaotic Mackey-Glass time series significantly verifies the performance of our algorithm.