计算机科学 ›› 2016, Vol. 43 ›› Issue (2): 9-12.doi: 10.11896/j.issn.1002-137X.2016.02.002

• 目次 • 上一篇    下一篇

一种时间序列分解的卫星周期性参数预测方法

周枫,皮德常   

  1. 南京航空航天大学计算机科学与技术学院 南京210016,南京航空航天大学计算机科学与技术学院 南京210016
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金-民航联合基金(U1433116),航空科学基金(20145752033)资助

Prediction Algorithm for Seasonal Satellite Parameters Based on Time Series Decomposition

ZHOU Feng and PI De-chang   

  • Online:2018-12-01 Published:2018-12-01

摘要: 卫星遥测参数预测对卫星故障发现有着重要的指导作用。针对周期性参数难以预测的问题,提出了一种基于时间序列分解的卫星周期性参数的预测方法。该方法首先在频域上使用小波分析对参数序列进行降噪并提取参数的周期;然后,在时域上对参数的时间序列进行分解,进一步得到参数的趋势项和随机项,并根据各项特点分别使用灰色模型和ARMA模型进行预测;最后,重组各部分的预测值,得到最终预测结果。通过对我国某卫星遥测数据的对比实验分析,验证了该方法的正确性和有效性。

关键词: 卫星参数,时间序列,预测,小波分析,灰色模型

Abstract: The prediction of satellite seasonal parameters plays a key guiding role for the satellite fault prediction.Considering the low prediction precision problem of satellite seasonal parameters,a prediction method for seasonal satellite parameters based on time series decomposition was proposed.Firstly,the wavelet analysis method is used to eliminate noise and extract the cycle from the parameter sequence in the frequency domain.Then,with the time series decomposition method in the time domain,the trend item and random item are generated.Thus the gray model(GM) and the auto regressive moving average model(ARMA) are used to predict these items respectively according to their characteristics.Finally,all the prediction parts are combined and the final predictive value is got.The contrast experimental prediction and analysis of satellite remote sensing data verify the effectiveness of the proposed method.

Key words: Satellite parameters,Time series,Prediction,Wavelet analysis,Grey model

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