遗传门限自回归模型在气象时间序列预测中的应用

APPLICATION OF GENETIC THRESHOLD AUTO-REGRESSIVEMODEL TO FORECASTING METEOROLOGICALTIME SERIES

提出了建立门限自回归模型（TAR）的一套简便通用的方法。用基于实码的改进遗传算法,可同时优化门限值和自回归系数,解决了TAR建模过程所涉及的大量复杂寻优工作这一难题,为TAR模型在气象预测中的广泛应用提供了有力工具。实例计算的结果说明:通过门限值的控制作用,TAR模型可有效地利用气象时序资料所隐含的时序分段相依性这一重要信息,限制了模型误差,保证了TAR模型预测性能的稳健性,提高了预测精度。该方法具有通用性,在各种气象非线性时序预测中具有广泛的实用价值。

A simple and general scheme is presented for establishing a threshold auto-regressive (TAR) model. Both threshold values and auto-regressive coefficients can be optimized with the genetic algorithm based on real coding genetic algorithm improved by the authors, and the difficulty problem of modeling of TAR is resolved, which gives a strong tool for widely using TAR model in meteorological forecasting. The calculation example shows that TAR model can effectively utilize the important information of the section interdependence during the meteorological time series observation dates by controlling threshold valves, reduce model errors, and ensure good stability and accuracy of the model forecasting. As a general method, the scheme has major theoretic valve and wide-ranging application to predicting of meteorological nonlinear time series.