论文标题
Hilbertian自回归过程的预测：一种反复的神经网络方法
Prediction of Hilbertian autoregressive processes : a Recurrent Neural Network approach
论文作者
论文摘要
丹尼斯·博斯克（Denis Bosq）在90年代初引入了自回归希尔伯特式模型（ARH）。这是大量文学的主题，并诞生了许多扩展。该模型概括了经典的多维自回归模型，该模型在时间序列分析中广泛使用。它成功地应用于金融，工业，生物学等众多领域。我们在这里建议使用神经网络学习方法根据自相关操作员的估计来比较经典的预测方法。后者基于复发性神经网络的流行版本：长期的短期内存网络。比较是通过模拟和真实数据集进行的。
The autoregressive Hilbertian model (ARH) was introduced in the early 90's by Denis Bosq. It was the subject of a vast literature and gave birth to numerous extensions. The model generalizes the classical multidimensional autoregressive model, widely used in Time Series Analysis. It was successfully applied in numerous fields such as finance, industry, biology. We propose here to compare the classical prediction methodology based on the estimation of the autocorrelation operator with a neural network learning approach. The latter is based on a popular version of Recurrent Neural Networks : the Long Short Term Memory networks. The comparison is carried out through simulations and real datasets.
