论文评价了SARFIMA模型在预测长周期高频长记忆时间序列上的有效性。通过与ARFIMA、ARMA和PAR模型的模拟比较，结果显示如果正确设定参数，SARFIMA模型的效果与其他三种模型无显著差别，但是如果使用WHI方法估计参数，SARFIMA模型预测效果比其他三种模型差。在对实际交易量序列进行实证分析时，SARFIMA模型效果最差，ARFIMA模型效果最好，而ARMA和PAR模型无论是在模拟和实际序列分析中都有很好的表现，表明短记忆模型对周期长记忆时间序列预测也有很好的效果。

This paper evaluates the efficiency of the SARFIMA model at forecasting high-frequency long memory series with especially long periods. Three other models, the ARFIMA, ARMA and PAR models, are also included to compare their forecasting performances with that of the SARFIMA model. For the artificial SARFIMA series, if the correct parameters are used for estimating and forecasting, the model performs as well as the other three models. However, if the parameters obtained by the WHI estimation are used, the performance of the SARFIMA model falls far behind that of the other models. For the empirical intraday volume series, the SARFIMA model produces the worst performance of all of the models, and the ARFIMA model performs best. The ARMA and PAR models perform very well both for the artificial series and for the intraday volume series. This result indicates that short memory models are competent in forecasting periodic long memory series.

Handong Li, Xunyu Ye.Forecasting High-Frequency Long Memory Series with Long Periods Using the SARFIMA Model [J].Open Journal of Statistics, 2015, 5: 66-74.