基于非平稳时序ARIMA模型的W频段雨衰预测

杨峰; 薛斌; 刘剑

doi:10.11999/JEIT150472

基于非平稳时序ARIMA模型的W频段雨衰预测

doi: 10.11999/JEIT150472

杨峰¹,
薛斌¹,
刘剑¹

基金项目:

航空科学基金(20120196002)

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- 被引次数: 0
出版历程
- 收稿日期: 2015-04-27
- 修回日期: 2015-07-20
- 刊出日期: 2015-10-19

Rain Attenuation Prediction at W Band Based on Non-stationary Time-series ARIMA Model

Funds:

The National Aerospace Science Foundation of China (20120196002)

摘要

摘要: 针对目前绝大多数雨衰预测模型仅验证到55 GHz，而经过验证的W频段预测模型相对较少，且存在模型表述复杂度高、计算量大的问题，该文提出一种结构简单、计算量小的实时预测方法。该方法基于ARIMA模型，利用非平稳雨衰时序中相邻时序间的相关性建立预测模型，对初始序列进行平稳性检验，通过差分变换将非平稳序列转化为平稳序列，并对平稳化后的时间序列进行参数估计及诊断检验，将传统非线性预测转化为线性预测。并先将该ARIMA(1,1,6)模型在不同极化方式、预测间隔和时序个数的条件下进行比较，然后分别与ITU-R, Silva Mello模型在垂直极化、预测间隔0.10 GHz，时序个数50的条件下进行比较，最后使用ARIMA(1,1,6)模型进行预测，并对照预测序列与仿真序列的吻合度。结果表明，ARIMA模型与ITU-R, Silva Mello模型所得结果预测误差不超过10-3 ，且衰减变化趋势基本相同，预测序列与仿真序列间吻合度较高，说明该方法可用于W频段雨衰预测，且预测精度高，模型表述简单。
- 雨衰预测 /
- W频段 /
- 非平稳时间序列 /
- ARIMA /
- 差分
Abstract: Most existing rain attenuation prediction models are only tested at 55 GHz. There are small numbers of tested W-band rain attenuation prediction models, but these tested models have issues with high complexity and large quantities of calculations. A real-time prediction method is proposed that has a simpler structure and smaller quantity of calculations. The proposed method is based on the ARIMA model, which utilizes the relationship among the time series to establish a prediction model, conducts a stationary test on the original time series, transforms the nonstationary time series into a stationary time series by using a difference transform, and estimates the parameters of the stationary time series. This sequentially transforms the traditional nonlinear prediction into a linear prediction. First, the ARIMA (1,1,6) model is compared under the conditions of different polarizations, prediction intervals, and numbers of time series. Then the proposed model is compared with the International Telecommunication Union-R (ITU-R) and the Silva Mello rain attenuation prediction models using the conditions of vertical polarization, a prediction interval of 0.10 GHz, and a number of 50 time series. Finally, the forecasting time series with the simulant series are compared. The result shows that the prediction error between the ARIMA model, the ITU-R model and the Silva Mello model does not exceed10-3, and that the change trend of the rain attenuation is basically the same. and the goodness of fit between the forecasting and simulant time series is good, which means that the proposed model can be applied to forecasting the rain attenuation in the W band, and that it has the advantages of simpler structure and high precision in prediction.
- Rain attenuation prediction /
- W band /
- Non-stationary time-series /
- ARIMA /
- Difference

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