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1·ARIMA model; Predict; Time series analysis; Hypertension; Incidence.

ARIMA模型；预测；时间序列分析；高血压；发病率。

2·The forecasting calculation results of additive model and ARIMA are compared.

并将叠合模型与ARIMA的预测结果进行了比较。

3·ARIMA model can be used to predict the non stable time series with adequate precision.

ARIMA模型可以较准确地预测非稳态随机过程的时间序列。

4·Third chapter recounts the intervention model type as well as ARIMA intervention model.

第三章详细阐述了干预模型的种类以及ARIMA干预模型的构建。

5·The ARIMA model has been applied to evaluate and predict the time series of macroscopic traffic volume.

应用ARIMA模型，对宏观交通量时间序列进行模型估计和预测。

6·It USES ARIMA model to analyze the trend of the BPI index in the future based on analyze it's fluctuate rule.

最后在分析BPI指数波动规律的基础上，利用ARIMA模型分析对该指数未来几期的走势进行了展望。

7·The results show that the seasonal ARIMA model is qualified for prediction of outdoor air control of VAV systems.

结果表明，季节性的ARIMA模型可以很好地满足空调系统新风预测的要求。

8·ConclusionThe ARIMA model can be used to forecast HFRS incidence with high predictive precision in the short-term.

结论ARIMA模型可用于预测H FRS月发病率，其短期预测精度较高。

9·The fourth chapter used the seasonal ARIMA model to fit and forecast in Shandong Province monthly price index data.

第四章利用季节ARIMA模型对山东省物价指数定基月度数据进行拟合，并进行预测。

10·Then, we use the ARCH model to analyze the market price index and find that the ARIMA model is better than the ARCH model.

然后运用ARCH模型进行分析，经过比较发现在对我国物价指数的分析上，ARIMA模型的效果要好于ARCH模型。