Forecasting with prediction intervals for periodic autoregressive moving average models

被引：29

作者：

Anderson, Paul L. ^{[1
]}

Meerschaert, Mark M. ^{[2
]}

Zhang, Kai ^{[2
]}

机构：

[1] Albion Coll, Albion, MI 49224 USA

[2] Michigan State Univ, E Lansing, MI 48824 USA

来源：

JOURNAL OF TIME SERIES ANALYSIS | 2013年 / 34卷 / 02期

关键词：

Periodic correlation; autoregressive moving average; forecasting; TIME-SERIES; RIVER FLOWS; LIKELIHOOD; ALGORITHM;

D O I：

10.1111/jtsa.12000

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

Periodic autoregressive moving average (PARMA) models are indicated for time series whose mean, variance and covariance function vary with the season. In this study, we develop and implement forecasting procedures for PARMA models. Forecasts are developed using the innovations algorithm, along with an idea of Ansley. A formula for the asymptotic error variance is provided, so that Gaussian prediction intervals can be computed. Finally, an application to monthly river flow forecasting is given, to illustrate the method.

引用

页码：187 / 193

页数：7

共 21 条

[1] Fourier-PARMA models and their application to river flows [J].