Finally, we give Bayesian analysis of the long memory time series ARFIMA model.
最后，对长记忆时间序列arfima模型进行了贝叶斯分析。
The results show that ARFIMA model has more accuracy and can be used in tourism forecast.
结果表明ARFIMA模型的精度最高，在旅游需求预测中有较强的实用性。
To solve the higher peak and fat tail phenomenon, immediate memory and asymmetric features, this paper formulate the volatility model of exchange rate returns using the ARFIMA-EGARCH-M model.
为了解决汇率收益率波动中的“尖峰厚尾”、中期记忆和非对称特征，提出了利用ARFIMA - EGARCH - M模型建立汇率收益率波动模型。
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