基于相空间重构的f_0F_2一步预测算法研究

One-step Ahead Prediction of f_0F_2 Based on Phase Space Reconstruction

利用低纬度地区某垂测站2005年3月和4月两个月f_0F_2参数共5856个数据样本,对提前15min的一步预测算法进行了研究.基于混沌时间序列的相空间重构方法,以相近邻点轨迹具有相似性为预测理论基础,采用k最近邻点的方法对下一时刻的f_0F_2进行预测.对邻点个数的选取采用了训练法和自适应选择法,对选出的邻点采用平均法和自回归两种方法进行处理,并对几种不同方法的预测结果进行了比较.结果证明,基于相空间重构的一步预测算法预测精度较高,并且容易实现,运算速度高,适用于电离层参数准实时预报.

One-step ahead prediction of critical frequency f0F2 is presented, using samples of about 5856 observations of f0F2 with 15-minute time resolution taken from a station in the low latitude, from March 2005 to April 2005. Based on phase space reconstruction of chaotic time series, and according to the theory that near trajectories have similar characteristics, the k-nearest neighbor method is taken to forecast f0F2 15 minutes ahead. Training method and self-adaptive-select method are attempted to define the neighbor numbers used for prediction, and for these neighbors, averaging method and regression ones are taken to treat them. The forecasting errors for those methods are compared. It is shown that one-step ahead forecasting method based on phase space reconstruction can reach high accuracy. In addition, the method has the advantage of easy to implementing with high processing speed. So it is quite suitable for quasi real-time prediction.