摘要
水质时序演变特征的研究及预测对制定合理可行的水污染防控措施有重要意义,但水质时序的结构复杂性和非平稳性是采用自回归模型进行预测的瓶颈。针对上述问题,作者将马尔可夫状态切换理论(Markov Switching)应用于水质时序的自回归建模预测。马尔可夫状态切换-自回归模型(MS-AR)是一种研究具有变结构动力特征的时间序列分析方法,对异方差时序有较强的适应性。实例运用中,首先对果河桥断面的氨氮时序进行Box-Cox变换,然后运用MS-AR模型对其进行结构分析及预测。结果表明:MS-AR模型能有效识别出该水质时间序列演变过程中的两种结构模式,通过与经典自回归模型的预测精度相比,该方法的各项指标均优,也说明该方法在水质时间序列动态结构分析和预测方面有良好的应用前景。
Evolution features research and multi-step prediction of water quality time series have important significance for making reasonable and applicable environmental protection measures.But the structural complexity and non-stationary is the bottleneck of auto-regressive model application to water quality series prediction.Focusing on these problems,Markov switching theory was introduced to combined with autoregressive model for water quality series simulating and predicting.Markov switching auto-regressive model（MS-AR） is suitable for the analysis of non-stationary time series with variable structure,and has a good adaptability to heteroscedastic time series.In the modeling the water quality time series of Guoheqiao section,the Box-Cox transformation was used to transform the sample data to make it approximately Gaussian,and then MS-AR model was applied to the analysis and prediction of the water quality sequence.The result showed that MS-AR model identified the two evolution patterns of the series,and the prediction of MS-AR was better than the classical AR model,which indicates that MS-AR model has a good prospect in water quality sequence dynamic structure analysis and its prediction.This model can automatically identify the regime switching of a time series’ evolution process.
出处
《吉林大学学报（地球科学版）》
EI
CAS
CSCD
北大核心
2010年第3期657-664,共8页
Journal of Jilin University：Earth Science Edition
基金
国家自然科学基金项目(50879051)
关键词
水质时序
马尔可夫理论
状态切换
Box-Cox变换
自回归模型
水污染
water quality time series
Markov theory
regime switching
Box-Cox transformation
autoregressive model
water pollution
