摘要:为了解决LMT时间序列出现的缺失和强干扰现象,根据实测资料数据量大、非线性、非平稳性等特点,首次采用ARIMA模型进行预测和填补,基于平稳性检验和贝叶斯信息准则确定模型阶数,采用最小二乘原理确定模型参数,建立双向预测模型和线性合并方法进行预测,并对比ARIMA模型和AR模型预测数据的准确度。实例表明,ARIMA模型预测结果准确,精度比AR模型高,且误差不会累积,解决了原始资料的不连续性和强干扰的问题。
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