| ARMA模型在哈尔滨气温预测中的应用 |
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| 引用本文： | 丛凌博,蔡吉花.ARMA模型在哈尔滨气温预测中的应用[J].数学的实践与认识,2012,42(16):190-195. |
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| 作者姓名： | 丛凌博 蔡吉花 |
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| 作者单位： | 黑龙江科技学院理学院,黑龙江哈尔滨,150027 |
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| 基金项目： | 黑龙江省教育厅科学研究项目 |
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| 摘 要： | 将时间序列分析引入到气温时间序列预测的研究中,深入分析气温样本数据,并对其建立ARMA模型.采用最佳准则函数法确定模型的阶数,并利用自相关函数对模型的残差进行了检验.通过条件期望预测和适时修正预测方法求得预测值,与真实值的比较得到适时修正预测精确度比条件期望预测的精确度高.
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| 关 键 词： | 时间序列分析 自相关函数 ARMA模型 气温预测 |
The Application of Auto-Regressive and Moving Average Model in Harbin Temperature Forecast |
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| CONG Ling-bo
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CAI Ji-hua.The Application of Auto-Regressive and Moving Average Model in Harbin Temperature Forecast[J].Mathematics in Practice and Theory,2012,42(16):190-195. |
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| Authors: | CONG Ling-bo CAI Ji-hua |
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| Institution: | (School of Science,Heilongjiang Institute of Science and Technology,Haerbin 150027,China) |
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| Abstract: | To research temperature time-series forecast time series analysis is introduced in this article,the ARMA model is established according to the deeply analysis of temperature sample data.The order number of model is decided by the best principles function method, residual of the model is checked using its self-correlation function.Finally,conditions expected forecast method and timely amendment forecasting method are used to forecast the temperature.Compare the results of two methods with the raw data,the accuracy of timely amendment forecasting method is higher. |
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| Keywords: | time series analysis autocorrelation function ARMA model temperature forecast |
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