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An Application of Dynamic Regression Model and Residual Auto-Regressive Model in Time Series

机译：动态回归模型和残差自回归模型在时间序列中的应用

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This paper, by using the dynamic regression model (ARIMAX) models and predicts the tourist date from 1979 to 2004 in Zhejiang Province, and makes stationary test and white noise test of the residual date generated by the above analysis. The innovation point of this paper is that it is suitable to establish dynamic regression by cointegration test and proves the data of the residual data validation is stationary. Further testing and analysis of residual data, finds that the residual data can establish auto-regression model. This method has made full use of data information. Thus the paper presents that the prediction effect of the combination of the dynamic regression model and the residual auto regressive model is superior to that of the prediction model of the ARMA model. This combination model has better adaptability, greatly improves the predicted effect of the model.
机译：本文利用动态回归模型（ARIMAX）对浙江省1979年至2004年的旅游日期进行了预测，并对上述分析产生的剩余日期进行了固定检验和白噪声检验。本文的创新点是适合通过协整检验建立动态回归，并证明残差数据验证的数据是平稳的。通过对残差数据的进一步测试和分析，发现残差数据可以建立自回归模型。该方法充分利用了数据信息。因此，本文提出了动态回归模型和残差自回归模型相结合的预测效果优于ARMA模型的预测模型。该组合模型具有较好的适应性，大大提高了模型的预测效果。

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