模糊回归组合模型在CPI预测中的应用

Fuzzy Regression Combination Model and Its Application in CPI Forecasting

摘要: 本文提出了一种模糊回归组合预测方法，并运用到我国CPI的预测中。该方法能综合模糊回归和组合预测的优点，给出更符合实际的CPI预测区间值。首先，选取二次曲线模型和时间序列模型作为单项预测模型，并将得到的预测结果模糊化。其次，在给定拟合度的条件下，通过极小化模糊度来建立模糊回归组合模型，求解最优权重系数。最后，CPI预测结果表明，模糊回归组合模型的预测结果优于各单项预测模型，能提高CPI的预测精度，适合对CPI进行短期预测。

Abstract: A fuzzy regression combination forecasting method is proposed and applied to the CPI forecasting in this paper. The method can synthesize the advantages of fuzzy regression and combination forecasting, and give a practical predicted interval value of CPI. Firstly, the quadratic curve model and the time series model be selected as the single prediction model, and the prediction results are fuzzified. Secondly, the fuzzy regression combination model is established by minimizing the fuzzy degree for the given goodness-of-fit to solve the optimal weight coefficient. Finally, the CPI forecasting results show that the fuzzy regression model is better than the single prediction model, which can improve the prediction accuracy of CPI and is suitable for short-term prediction of CPI.