| 基于调和分析和ARIMA-SVR的组合潮汐预测模型 |
| 投稿时间：2018-08-22 修订日期：2018-11-12 点此下载全文 |
| 引用本文：刘娇,史国友,朱凯歌,张加伟,李爽,陈作桓,王伟.基于调和分析和ARIMA-SVR的组合潮汐预测模型[J].上海海事大学学报,2019,40(3):93-99. |
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| 基金项目:国家自然科学基金（51579025）；辽宁省自然科学基金（20170540090） |
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| 中文摘要:为提高潮汐预测精度，解决单一调和分析预测精度不高的问题，提出一种基于调和分析和自回归综合移动平均 支持向量回归机（autoregressive integrated moving average support vector machine for regression，ARIMA SVR）的组合潮汐预测模型。潮汐分析中，潮汐可认为是由受引潮力影响的天文潮位和受环境因素影响的非线性水位的叠加。采用小波分析对潮汐样本数据进行去噪处理，使用调和分析法计算天文潮位，以调和分析法计算产生的残差作为非线性水位样本数据,并使用ARIMA SVR模型进行潮高计算，最后将两部分的计算结果进行线性求和得到最终的潮汐预测值。利用美国旧金山港口实测潮汐数据进行预测仿真，结果表明，该组合模型解决了调和分析忽略非线性影响的问题，提高了潮汐预测准确率，可行且高效。 |
| 中文关键词:潮汐预测 组合模型 调和分析法 支持向量回归机（SVR） 自回归综合移动平均（ARIMA）模型 |
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| A combined tide prediction model based on harmonic analysis and ARIMA-SVR |
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| Abstract:To improve the accuracy of tide prediction and solve the problem of low accuracy of single harmonic analysis, a combined tide prediction model based on the harmonic analysis and the autoregressive integrated moving average support vector machine for regression (ARIMA SVR) is proposed. In tide analysis, tide can be considered as the superposition of astronomical tide level affected by tide generating force and non linear water level affected by environmental factors. The wavelet analysis is used to denoise the tide sample data. The harmonic analysis method is used to calculate the astronomical tide level. The residual sequence generated by the harmonic analysis method is used as the sample data of non linear water level, and ARIMA SVR model is used to calculate the tide height. The tide prediction value is obtained by linear summation of the calculated results of the two parts. The simulation of prediction is carried out using measured tide data of San Francisco Port of the United States. The results show that: the combined model solves the problem of ignoring nonlinear effects in the traditional harmonic analysis, and the accuracy of tide prediction is improved; the combined model is feasible and efficient. |
| keywords:tide prediction combined model harmonic analysis method support vector machine for regression (SVR) autoregressive integrated moving average (ARIMA) model |
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