基于Markov-SVR模型的火灾时间序列预测

Ye Zhang, Wen Tian, S. Liu
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引用次数: 0

摘要

基于支持向量回归和马尔可夫状态转换,提出了一种新的预测模型——马尔可夫支持向量回归(MSVR)模型来预测火灾时间序列。在本文提出的模型中,支持向量回归从一系列火灾数据中构建最优预测模型,然后使用马尔可夫状态转移来减小模型产生的残差。利用实际火灾时间序列数据对所提出的模型进行了检验。结果表明,MSVR模型比纯SVR模型获得了更好的结果性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fire Time Series Forecasting Based on Markov-SVR Model
Based on support vector regression and Markovstate transition, a new prediction model termed as Markovsupportvector regression (MSVR) model is proposed toforecast the fire time series. In the proposed model, a SVR is tobuild an optimal prediction model from a series of fire data,and then uses the Markov state transition to reduce theresiduals errors produced by the mentioned model. Theproposed model is examined using actual fire time series data.The results show that the MSVR model gets the better resultperformance than that of the pure SVR model.
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