1) multiplicative seasonal Autoregressive Integrated Moving Average
乘积季节ARIMA
1.
Based on the characteristics exhibited in the traffic series,multiplicative seasonal Autoregressive Integrated Moving Average mode(l ARIMA) is employed to make traffic series prediction.
深入研究了某省某移动网络运营商的多年的话务量数据,利用自相关函数对其周期性和趋势性方面的规律进行了探测,并在此基础上提出应用乘积季节ARIMA模型进行建模和预测的方案。
2) multiple seasonal ARIMA model
ARIMA季节乘积模型
1.
Objective:To establish a model of multiple seasonal autoregressive integrated moving average(ARIMA) (p,d,q)(P,D,Q) s on month-morbility of Bacillary Dysentery,and to explore the applications of multiple seasonal ARIMA model.
结论:通过ARIMA(0,0,1)(0,1,1)12模型与ARIMA(0,1,1)12模型对细菌性痢疾月发病数预测效果的比较,表明ARIMA季节乘积模型是一种短期预测精度较高的预测模型。
3) multiple seasonal ARIMA
乘积季节性ARIMA
1.
In this paper, the product of the multiple seasonal ARIMA model with the previous comparison of the scheduling method, uses the heating system has been collecting real-time data to forecast the next sampling time values.
本文采用乘积季节性ARIMA模型与以往其他的调度方法进行比较,利用采集得到的供热系统的实时数据来预报下一个采样时刻的预报值,并以此为依据用于供热系统的优化调度,将对供热系统的节能运行和实时控制起到重要作用。
4) ARIMA seasonal multiple model
ARIMA季节乘积混合模型
1.
Presented in this paper are traffic prediction models based on application layer,which use ARIMA seasonal multiple model(p,d,q)(P,D,Q)s for modeling and forecasting the seasonal time series from China\'s exports of a metro area network link.
因此提出基于应用层的流量预测分析模型,对国内某城域网出口链路上的应用层流量序列采用ARIMA季节乘积混合模型(p,d,q)(P,D,Q)s建模并预测。
5) seasonal ARIMA
季节ARIMA
1.
Integrating regression analysis with time series analysis, a regression model with seasonal ARIMA errors — Regression-Time Series Analysis model — was presented to forecast the short-term freight.
应用此回归-时序混合模型进行月度货运量的拟合预测,并与多元线性回归模型和季节ARIMA模型的拟合预测结果相比较,表明回归-时序混合模型可以提高短期货运量的预测精度。
6) multiple ARIMA
乘积ARIMA
1.
Prediction of the uncertain demand for productby a multiple ARIMA model;
基于乘积ARIMA模型的产品不确定性需求预测
补充资料：乘积
1.两个或两个以上的数相乘所得的数。简称积。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条