In order to effectively improve the accuracy of Consumer Price Index (CPI) prediction so as to more truly reflect the overall level of the country’s macroeconomic situation, a CPI big data prediction method based on wavelet twin support vector machine (SVM) is proposed. First, the historical CPI data are decomposed into high-frequency part and low-frequency part by wavelet transform. Then a more advanced twin SVM is used to build a prediction model to obtain two kinds of prediction results. Finally, the wavelet reconstruction method is used to fuse the two kinds of prediction results to obtain the final CPI prediction results. The wavelet twin SVM model is used to fit and predict CPI index. Experimental results show that compared with the similar prediction methods, the proposed prediction method has higher fitting accuracy and smaller root mean square error.

中文翻译：

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基于小波孪生支持向量机的CPI大数据预测

为有效提高CPI预测的准确性，更真实地反映国家宏观经济形势的整体水平，提出了一种基于小波孪生支持向量机（SVM）的CPI大数据预测方法。首先，通过小波变换将历史CPI数据分解为高频部分和低频部分。然后使用更高级的孪生SVM建立预测模型，得到两种预测结果。最后采用小波重构方法对两种预测结果进行融合，得到最终的CPI预测结果。小波孪生SVM模型用于拟合和预测CPI指数。实验结果表明，与类似的预测方法相比，