基于集合经验模式分解的火灾时间序列预测

采用集合经验模式分解(EEMD)和多变量相空间重构技术，结合非线性支持向量回归(SVR)模型，提出一种火灾次数时间序列组合预测方法。根据EEMD将非平稳的火灾时间序列分解为一系列不同尺度的固有模态分量，利用多变量相空间重构技术对分解的各个分量进行相空间重构，构建其训练数据，对重构的训练数据建立各分量的非线性支持向量回归预测模型，使用SVR集成预测方法对火灾时间序列进行预测。仿真结果表明，与单变量相空间重构方法以及SVR方法相比，该方法具有较高的预测精度。

Fire Time Series Forecasting Based on Ensemble Empirical Mode Decomposition

Based on a combination of Ensemble Empirical Mode Decomposition(EEMD) and multivariate phase space reconstruction, a new combined forecasting model is proposed for fire time series by using Support Vector Regression(SVR). The fire time series is decomposed into a series of Intrinsic Mode Function(IMF) in different scale space by using EEMD. The phase space of IMF is reconstructed by using of multivariate phase-space reconstruction. Based on nonlinear SVR, a prediction model is developed for each intrinsic mode functions, and these forecasting results of each IMF are combined with SVR again to obtain final forecasting result. Experimental results show that this method is more accurate than single variable phase space reconstruction method and SVR method.