COMPUTATION AND CHARACTERIZATION OF AUTOCORRELATIONS AND PARTIAL AUTOCORRELATIONS IN PERIODIC ARMA MODELS

QIN SHAO; ROBERT LUND

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COMPUTATION AND CHARACTERIZATION OF AUTOCORRELATIONS AND PARTIAL AUTOCORRELATIONS IN PERIODIC ARMA MODELS

机译：周期ARMA模型中自相关和部分自相关的计算和表征

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This paper studies correlation and partial autocorrelation properties of periodic autoregressive moving-average (PARMA) time series models. An efficient algorithm to compute PARMA autocovariances is first derived. An innovations based algorithm to compute partial autocorrelations for a general periodic series is then developed. Finally, periodic moving averages and autoregressions are characterized as periodically stationary series whose autocovariances and partial autocorrelations, respectively, are zero at all lags that exceed some periodically varying threshold.

机译：本文研究了周期性自回归移动平均（PARMA）时间序列模型的相关性和部分自相关性。首先推导了一种有效的计算PARMA自协方差的算法。然后开发了一种基于创新的算法来计算一般周期序列的部分自相关。最后，周期性移动平均值和自回归的特征是周期性平稳序列，其自协方差和部分自相关在所有超过某个周期性变化阈值的滞后处均为零。

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来源
《Journal of Time Series Analysis》 |2004年第3期|p.359-372|共14页
作者
QIN SHAO; ROBERT LUND;
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作者单位

The University of Georgia;

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原文格式 PDF
正文语种 eng
中图分类数理科学和化学;
关键词
periodic series; PARMA model; autocovariances; partial autocorrelations; innovations algorithm; durbin-levinson algorithm;

机译：周期序列;PARMA模型;自协方差;部分自相关;创新算法;Durbin-levinson算法;

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COMPUTATION AND CHARACTERIZATION OF AUTOCORRELATIONS AND PARTIAL AUTOCORRELATIONS IN PERIODIC ARMA MODELS

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