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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
The repo rate of the national bond is analyzed and ARIMA and GARCH models related to the rate are established in this paper.
以国债回购利率为研究对象，分别建立ARIMA及GARCH模型，并比较这两种模型的预测能力。
Objective To discuss the application of seasonal time series ARIMA predictive model and fit predictive model of HFRS incidence.
目的探讨季节性时间序列ARIMA预测模型在时间序列资料分析中的应用，建立HFRS发病率的预测模型。
Based on the ARIMA model, this article has forecasted the employment number of three industrial sectors in Beijing during 2007-2010.
本文通过建立北京市三次产业就业人数的时间序列arima模型，对北京市2007年- 2010年的三次产业吸纳的就业人数进行了预测。
Objective To explore the application of seasonal time series ARIMA model in prediction of malaria incidence in an unstable malaria area.
目的探讨应用季节性时间序列ARIMA模型预测非稳定性疟区疟疾发病率的可行性。
Furthermore, according to cell traffic changes in one day cycle, the multiple seasonal ARIMA model of the GPRS cells traffic was proposed.
进而利用小区流量以天为周期变化的特点，得到了流量变化的乘积季节ARIMA模型。
Conclusion: ARIMA model can be used to exactly fit the changes of the incidence of measles and predict the future measles incidence in future.
结论：ARIMA模型能很好的模拟深圳市麻疹发病率的变动趋势，预测效果可靠。
Objective:To establishment the SAS procedure of ARIMA Model and to investigate the application of ARIMA predictive model in seasonal time series.
目的：利用SAS程序实现ARIMA模型，探讨ARIMA预测模型在季节性时间序列资料分析中的应用。
This thesis apply X-12-ARIMA seasonal adjustment model to analyze the economics effect of holidays, which has been used widely in Europe and USA.
本文利用X - 12 -ARIMA季节调整模型来分析节假日的经济效应。这一模型在美国、欧洲已得到广泛的应用。
In this paper, tentative research on the fact of ARIMA theory and its application in the aspect of green farm produce market price data processing.
本文对ARIMA理论及其在绿色农产品市场价格数据处理中的应用作了尝试性的研究。
Aim at the trait of time series, investigate the different ARIMA patterns, put forward the ARIMA model and forecast and estimate aim at special market.
针对其时间序列的特点，研究了ARIMA的不同模式，提出了面向特定市场的ARIMA模型，及其预测和估计方法。
Heavy metal pollution in mining areas possesses the character of time series, so time series ARIMA model can be used to forecast heavy metal pollution.
矿区重金属污染具有时间序列的特征，因此可以采用时间序列arima模型对重金属污染进行预测。
Arima purification will be unremitting efforts, our professional, dedicated entrepreneurial spirit, and strive to purify the industry to add brilliance.
华宇净化将以不懈的努力、秉承专业、敬业的企业精神，努力为净化行业增添光彩。
The application examples show that ARIMA model has the advantages of high accuracy, reliable data, easy operation, high working speed, high adapting ability.
实例表明，应用ARIMA模型进行需求预测具有精度高、数据可靠、操作方便、运行迅速、应变能力强等优点。
Based on the WTI price data between the end of 2002 and the beginning of 2006, an ARIMA model is built to make a forecast of the tendency of petroleum price.
以2002年年末至2006年年初的WTI原油价格数据为基础，构建ARIMA模型并对2006年度的油价走势进行分析和预测。
Based on the WTI price data between the end of 2002 and the beginning of 2006, an ARIMA model is built to make a forecast of the tendency of petroleum price.
以2002年年末至2006年年初的WTI原油价格数据为基础，构建ARIMA模型并对2006年度的油价走势进行分析和预测。
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