| The Study of a New Method for Forecasting Non—stationary Series |
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| 引用本文： | 陈萍,Zhang Jie.The Study of a New Method for Forecasting Non—stationary Series[J].高技术通讯(英文版),2002,8(2):47-50. |
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| 作者姓名： | 陈萍 Zhang Jie |
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| 作者单位： | [1]FirstInstituteofOceanography,SOA,Qingdao266061,P.R.China [2]KeyLaboratoryofMarineScienceandNumericalModeling,SOA,Qingdao266061,P.R.China |
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| 基金项目： | 国家高技术研究发展计划(863计划) |
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| 摘 要： | A new method for forecasting non-stationary series is developed.Its steps are as follows.Step 1.Data delaminating.Non-stationary series is delaminated into several multi-scale steady data layers and one trend layer.Step 2.Modeling and forecasting each stationary data layer.Step 2.Imitating trend layer using polynomial.Step4.Combining the forecasting layers and imitating layer into one series,The EMD(Empirical Mode Decomposition) method suitable to preocess non-stationary series is selected to delaminate data,while ARMA(Auto Regressive Moving Aver age)model is employed to model and forecast stationary data layer and least square error method for trend layer regression.Aiming at forecasting length,forecasting orientation and selective method,experiments are performed for SAR(Synthetic Aperture Radar) images.Finally,an example is provided,in which the whole SAR image is restored via the method proposed by this paper.
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| 关 键 词： | 雷达图象 非稳定时序 数据分层 预报 |
The Study of a New Method for Forecasting Non-stationary Series |
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| Zhang Jie.The Study of a New Method for Forecasting Non-stationary Series[J].High Technology Letters,2002,8(2):47-50. |
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| Authors: | Zhang Jie |
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| Abstract: | A new method for forecasting non-stationary series is developed. Its steps are as follows: Step 1. Data delaminating. Non-stationary series is delaminated into several multi-scale steady data layers and one trend layer. Step 2. Modeling and forecasting each stationary data layer. Step 3. Imitating trend layer using polynomial. Step 4. Combining the forecasting layers and imitating layer into one series. The EMD (Empirical Mode Decomposition) method suitable to process non-stationary series is selected to delaminate data, while ARMA (Auto Regressive Moving Average) model is employed to model and forecast stationary data layer and least square error method for trend layer regression. Aiming at forecasting length, forecasting orientation and selective method, experiments are performed for SAR (Synthetic Aperture Radar) images. Finally, an example is provided, in which the whole SAR image is restored via the method proposed by this paper. |
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| Keywords: | non-stationary series forecasting data delaminating ARMA model EMD SAR image |
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