现代电子技术2018，Vol.41Issue(1)：43-46,4.DOI:10.16652/j.issn.1004-373x.2018.01.010
基于粒子滤波的混沌时间序列局域多步预测
Particle filtering based local multi-step prediction for chaotic time series
摘要
Abstract
It has important value and practicability(such as stock forecasting,rainfall forecasting and temperature fore-casting)to predict the chaotic time series. It is difficult to predict the chaotic time series due to its uncertainty and realization of multi-step prediction. The least square method is used to solve the model parameters and perform local prediction for the chaotic time series,but has low prediction accuracy. In order to improve the accuracy of local linear prediction,a local multi-step pre-diction method based on particle filtering(PF)is proposed for chaotic time series. The particle filtering is adopted to optimize the parameters to obtain more accurate optimization model for multi-step prediction. Simulation results show that the multi-step and single-step prediction effects of this method are improved significantly.关键词
局域线性预测/混沌时间序列/粒子滤波/多步预测/邻近点/预测误差Key words
local linear prediction/chaotic time series/particle filtering/multi-step prediction/adjacent point/predition error分类
信息技术与安全科学引用本文复制引用
姜娇娇,郭俊,杨淑莹..基于粒子滤波的混沌时间序列局域多步预测[J].现代电子技术,2018,41(1):43-46,4.基金项目
国家自然科学基金(61001174) （61001174）
天津市科技支撑和天津市自然基金(13JCYBJC17700)Project Supported by Nature Science Foundation of China (61001174), Tianjin Science and Technology Support and Nature Science Foundation of Tian-jin (13JCYBJC17700). （13JCYBJC17700）
