非平稳时间序列的区域预测研究

基于重构状态空间理论和嵌入定理,给出一个新的非平稳场时间序列的区域预测方法。该方法将外强迫因子引入到预测模型中,并且将区域内预测相点的周围相点所对应的空间信息也引入到预测模型中。然后利用该方法对33模Lorenz系统得到的"理想"的非平稳场时间序列进行预测实验分析。结果表明,嵌入外强迫因子可以更好地重构出原来的动力系统,有效地提高非平稳时间序列的预测精度;同时引入空间和外强迫信息可以利用空间数据弥补时间序列长度的不足,从而进一步提高预测精度。

Regional Prediction of Non-stationary Time Series

Based on state space reconstruction and the embedding theory, a new regional prediction method of non-stationary time series is presented. External forcing factors are embedded in the model, and the corresponding space information regarding to the predict phase point in this region is also imported into the model. Using this method, an ideal non-stationary time series from the 33-mode Lorenz system is analyzed. The results show that by embedding external forcing factors, the original dynamic system can be reconstructed efficiently, and the prediction accuracy of the non-stationary time series can be effectively developed. Space information can offset the deficiencies of time series length by embedding both space information and external forcing factors, which will further improve the prediction accuracy.