物理学报Issue(12)：1-9,9.DOI:10.7498/aps.62.120511
过滤窗最小二乘支持向量机的混沌时间序列预测*
Chaotic time series prediction using filtering window based least squares support vector regression∗
摘要
Abstract
When the traditional strategy of sliding window (SW) deals with the flowing data, the data far from current position are me-chanically and briefly moved out of the window, and the nearest ones are moved into the window. To solve the shortcomings of this forgetting mechanism, the strategy of filtering window (FW) is proposed, in which adopted is the mechanism for selecting the superior and eliminating the inferior, thus resulting in the data making more contributions to the will-built model to be kept in the window. Merging the filtering window with least squares support vector regression (LSSVR) yields the filtering window based LSSVR (FW-LSSVR for short). As opposed to traditional sliding window based LSSVR (SW-LSSVR for short), FW-LSSVR cuts down the computational complexity, and needs smaller window size to obtain the almost same prediction accuracy, thus suggesting the less computational burden and better real time. The experimental results on classical chaotic time series demonstrate the effectiveness and feasibility of the proposed FW-LSSVR.关键词
混沌时间序列/支持向量机/滑动窗/过滤窗Key words
chaotic time series/support vector machine/sliding window/filtering window引用本文复制引用
赵永平,张丽艳,李德才,王立峰,蒋洪章..过滤窗最小二乘支持向量机的混沌时间序列预测*[J].物理学报,2013,(12):1-9,9.基金项目
国家自然科学基金(批准号:51006052)和南京理工大学“卓越计划”“紫金之星”资助的课题 （批准号:51006052）
