摘要
为了对随机型时间序列进行预测,在分析其性质的基础上,提出了将其分解为趋势项、周期项和平稳随机项,建立时间序列分解预测模型,分别对趋势项和周期项进行检验和提取,利用自回归模型人工合成新的序列对平稳随机项进行模拟和预测。将该模型应用于降雨量预测,取得良好效果,表明了该模型的有效性和适用性。
In order to predict the stochastic time series, the time series is divided into trend component, periodic component and steady random component based on the analysis of its characteristic, respectively. A decomposition prediction model for rainfall is established in this paper. The trend component and period component of the time series are validated and extracted, respectively, and autoregressive model is applied for new artificial serial generation to simulate and predict the steady random component. Applying the model for the rainfall time series prediction, good results are derived, which demonstrates the validity and applicability of the model.
出处
《中国农村水利水电》
北大核心
2007年第9期6-8,共3页
China Rural Water and Hydropower
基金
国家重点基础研究发展计划("973"计划)资助项目(2003CB415206)
关键词
时间序列
分解预测模型
随机项
自回归模型
time series
decomposition prediction model
random component
autoregressive model
