Computer Science ›› 2016, Vol. 43 ›› Issue (2): 9-12.doi: 10.11896/j.issn.1002-137X.2016.02.002

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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

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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