1) seasonal ARIMA model
季节ARIMA模型
1.
Study of electricity sales forecasting for North China Power Grid based on seasonal ARIMA model
基于季节ARIMA模型的华北电网售电量预测研究
2.
In order to improve the accuracy of seasonal highway traffic volume forecasting,a general expression of seasonal ARIMA model with periodicity was presented based on the normal ARIMA model,and then the procedures of modeling and forecasting via seasonal ARIMA model were provided.
与三个常用季节预测模型:分组回归模型、可变季节指数预测模型和季节周期回归模型相比,季节ARIMA模型的预测精度最高。
3.
A seasonal ARIMA model was used to simulate the time series of Lianyungang coastal SST based on the monthly SST from 1996 to 2007,and the optimal model ARIMA(1,0,1)(0,1,0)12 was finally established with its structure determined according to the criteria of residual un-correlation and concision.
基于1996—2007年逐月时间序列数据,采用季节ARIMA模型对连云港近海表层水温时间序列进行模拟,并依据残差不相关和简洁性原则确定模型的结构,建立最优预测模型ARIMA(1,0,1)(0,1,0)12。
2) seasonal ARIMA models
季节ARIMA模型
1.
Since power load has obvious seasonal character,seasonal ARIMA models can be applied to capturing the power hehavior.
将季节ARIMA模型引入电力负荷的建模及预报,为电力资源分配的宏观调控及电网改造提供了一种可靠的方法和途径。
3) 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。
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) 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。
补充资料：ARIMA模型
ARIMA模型
【ARD城A模型]亦译“综合自回归移动平均模型”。考虑了时间序列的自回归特性和偏自回归特性，将自回归和移动平均分析技术结合起来的综合分析技术。是一种比较成熟的单变量平稳时间序列分析预测方法。 在AR模型和MA模型中，所考察的随机过程本身要求是平稳的，否则，通过差分使过程达到平稳后进行相应的分析。而ARIMA模型就是把差分、服模型和MA模型结合在一起的模型，实际上是更为一般的表达式，用ARIMA(p，d，q)表示，其中的d是表示原过程本身达到平衡所需的差分次数。如果原过程用xt表示，经d阶差分后获得平稳过程wt，即Wt=△dxt，则称xt为d阶齐次非平稳过程。完整的AR侧认(p，d，q)模型表示为: 了茂=吻十艺犷二1‘ .了’一i一名r二1民et一j+“: A印叫认(p，d，q)模型的参数估计一般是先确定原过程的平稳性，经d次差分获得平稳序列戮后，使用戮的自相关函数和偏自相关函数确定AR过程和MA过程的阶数(p，q)，然后估计模型的参数并进行相应的检验，确定模型的有效性，最后才可用于对原过程未来值的预测。
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参考词条