基于EEMD-ARIMA模型的气温预测研究
Prediction of Temperature Based on EEMD-ARIMA Model
摘要: 采用集合经验模态分解(EEMD)与差分整合移动平均自回归(ARIMA)模型相结合的方法对年均气温数据进行建模预测。首先对年均气温做加噪处理后进行经验模态分解(EMD)，使其分量平稳化，然后再对各分量采用ARIMA模型预测，最后将各预测结果叠加得到年均气温的预测值。通过比较发现，EEMD-ARIMA模型比EMD-ARIMA模型及ARIMA模型的预测结果具有更高的精确度。
Abstract: The average annual temperature data are modeled and predicted by ensemble empirical mode decomposition (EEMD) and autoregressive integrated moving (ARIMA) model. First, this paper performs the noise processing on the average annual temperature, and performs empirical mode decomposition (EMD) to make the components smoothed. Then, each component is predicted by ARIMA model. Finally, the component prediction results are added to obtain the final predicted value of the average annual temperature. By comparison, it is found that the prediction results of the EEMD-ARIMA model are more accurate than the prediction results of the EMD-ARIMA model and the ARIMA model.
文章引用：贾雨杰, 陈鹏蕾, 朱莉. 基于EEMD-ARIMA模型的气温预测研究[J]. 统计学与应用, 2020, 9(2): 304-311. https://doi.org/10.12677/SA.2020.92033

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