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 predicting effects of Grey Model and ARIMA Model are best among the 5 models.
两试点以灰色预测和ARIMA模型拟合效果较好。
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。
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。
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。
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。
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。
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。
Through ARIMA model and standardization, the non stationary vibration series acquired in the field were transformed to stationary time series normally distributed.
将现场测得的非平稳振动序列通过ARIMA模型和标准化处理，转化成标准正态平稳时间序列。
A general expression of seasonal ARIMA models with one periodicity is given, and procedures to model and predict traffic flow using seasonal ARIMA models are provided.
介绍了具有周期的季节ARIMA模型的一般表达方式，并提供了使用这一模型进行建模和预报的一般过程。
Then algorithm analysis of network traffic model, a brief introduction of the Poisson model, Markov model, ar, MA, ARMA model, focused on analyzing ARIMA model algorithm.
接着对网络流量模型算法分析，简单介绍了泊松模型，马尔科夫模型，AR,MA,ARMA模型，重点分析了ARIMA模型算法。
Conclusion the ARIMA model can be used to effectively predict the incidence of bacillary dysentery in Shaanxi. More original data are needed in order to optimize the model.
结论ARIMA模型可以较好地预测陕西省细菌性痢疾的发病趋势，模型预测效果的优化有待原始数据的进一步积累。
Based on the requirement for establishing coordinated atomic time scale at Shangha1 Observatory, the application of ARIMA model to the forecasting of atomic time was discussed.
根据上海天文台协调原子时尺度建立的要求，探讨了ARIMA模型在原子时预报中的应用。
As the example of the single vegetable species cabbage, its price problem is studied quantificationally in the facts of identification, diagnose, mimic and forecasting by using ARIMA model.
从研究单一蔬菜品种卷心菜开始，利用ARIMA理论和方法，从模型的识别、诊断、拟合与预测定量地研究其价格的问题。
Aiming at the actual difficulties in load forecasting, a new load forecasting method in which the solar term load is used as modeling data was put forward combining ARIMA model and BP network.
针对电力负荷预测的实际困难，提出了一种以节气负荷作为建模数据，将ARIMA模型及BP网络相结合的负荷预测新方法。
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 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。
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。
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