应用自激励门限自回归模式对旱涝游程序列的模拟和预报

MODELING AND PREDICTION CONCERNING TIME SERIES OF FLOOD/DROUGHT RUNS BY MEANS OF THE SELF-EXCITING THRESHOLD AUTOREGRES-SIVE MODEL

在用AR、ARMA等线性模式对气候序列进行拟合和预报时,由于气候序列中存在着非线性变化,所以拟合和预报效果往往不太理想。本文首次用非线性自激励门限自回归模式(SETAR)对由北京511年(1470—1980年)历史旱涝记录变换的湿涝(干旱)游程记录进行了模拟和预报,解决了长期以来预报方程不能随转折点变更的问题。拟合和预报结果表明:门限自回归模式的拟合和预报效果比线性AR模式有明显提高。AR模式只能预报出2年长度以下的游程转折点,而SETAR模式能较准确地预报出3年长度以上的游程转折点。这可能是因为在预报过程中SETAR模式能按游程转折点更新模式,而且模式建立时不要求序列具有平稳性的缘故。

Using linear regressive models (e. g. AR, ARMA model) to fit and predict the climatic time series, the results are not sufficiently good because there exist nonlinear variations in the time series. In this paper, a nonlinear self-exciting threshold autoregressive (SETAR) model is applied to modeling and predicting of the time series of flood/drought runs in Beijing, this time series being derived from the graded historical flood/drought records of the last 511 years (1470-1980). The results show that the modeling and predicting effects of the SETAR model are much better than that of the AR model. The AR model can predict the flood/drought runs, the lengths of which are only below two years, while the SETAR model can predict run-lengths over three years. This may be due to the effects that the SETAR model can renew the model according to the run-turning points in the process of prediction, though the time series is nonsta-tionary.