Author:
Tang Zhou-Jin ,Ren Feng ,Peng Tao ,Wang Wen-Bo ,
Abstract
This paper analyzes the error characteristic of traditional support vector machine prediction algorithm, where the error series are smooth and regular. This is because a single prediction model is incapable of fitting chaotic system mapping function and omitting some deterministic component. On this basis, a prediction algorithm that consists of an iterative error correction and a least square support vector machine (LSSVM) is proposed. The algorithm creats multiple predictive models via the method of iterative error correction to approximate the chaotic system mapping function and obtain significant improvements of predictive performance. In addition, the optimal parameters of the prediction model are automatically obtained from the pattern search algorithm which is simple and effective. Experiment conducted on Lorenz time series and MackeyGlass time series indicates that the proposed algorithm has a much better performance than that recorded in the literature.
Publisher
Acta Physica Sinica, Chinese Physical Society and Institute of Physics, Chinese Academy of Sciences
Subject
General Physics and Astronomy
Reference20 articles.
1. Chen D Y, Liu Y, Ma X Y 2012 Acta Phys. Sin. 61 100501 (in Chinese) [陈帝伊, 柳烨, 马孝义 2012 物理学报 61 100501]
2. Han M, Xu M L 2013 Acta Phys. Sin. 62 120510 (in Chinese) [韩敏, 许美玲 2013 物理学报 62 120510]
3. Song T, Li H 2012 Acta Phys. Sin. 61 080506 (in Chinese) [宋彤, 李菡 2012 物理学报 61 080506]
4. Lei Z, Fengchun T, Shouqiong L, Lijun D, Xiongwei P, Xin Y 2013 Sensors and Actuators B: Chemical 182 71
5. Rohitash C, Mengjie Z 2012 Neurocomputing 86 116
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