基于最优样本子集的在线模糊LSSVM混沌时间序列预测

Chaotic Time Series Prediction Based on Optimal Training Subset Online Fuzzy LSSVM

提出一种基于最优样本子集的在线模糊最小二乘支持向量机(least squares support vector machine,LSSVM)混沌时间序列预测方法.算法选择与预测样本时间上以及欧氏距离最近的样本点构成最优样本子集,并采用ε不敏感函数对其进行模糊化处理,通过模糊LSSVM训练获得预测模型.随着时间窗口的滑动,最优样本子集和预测模型实时更新,模型更新采用分块矩阵方法降低运算复杂度.实验中对时变Ikeda序列进行预测,表明所提出的方法与离线和在线LSSVM相比,训练速度更快,预测精度更高.

An optimal training subset online fuzzy least squares support vector machine （OTSOF-LSSVM） is proposed for chaotic time series prediction. Samples nearest to the prediction sample in both time and space are chosen to form the optimal training subset. An s-insensitive function is introduced to formulate the fuzzy membership. Thus a prediction model is established by fuzzy LSSVM. The subset and model are updated with the moving time window. Computational complexity is reduced by matrix partitioning. Experiment of predicting the time-variant chaotic time series Ikeda shows that the proposed method has better accuracy and high training speed as compared to offline and online LSSVM.