Analysis of time series data has been a challenging research subject for decades. Email traffic has recently been modelled as a time series function using a Recurrent Neural Network (RNN) and RNNs were shown to provide higher prediction accuracy than previous probabilistic models from the literature. Given the exponential rise of email workloads which need to be handled by email servers, in this paper we first present and discuss the literature on modelling email traffic. We then explain the advantages and limitations of different approaches as well as their points of agreement and disagreement. Finally, we present a comprehensive comparison between the performance of RNN and Long Short Term Memory (LSTM) models. Our experimental results demonstrate that both approaches can achieve high accuracy over four large datasets acquired from different universities’ servers, outperforming existing work, and show that the use of LSTM and RNN is very promising for modelling email traffic.

中文翻译：

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使用RNN和LSTM模型对电子邮件流量工作负载进行建模

几十年来，时间序列数据的分析一直是一个具有挑战性的研究课题。电子邮件流量最近已使用递归神经网络（RNN）建模为时间序列函数，并且与文献中先前的概率模型相比，RNN具有更高的预测准确性。鉴于需要由电子邮件服务器处理的电子邮件工作量呈指数级增长，在本文中，我们首先介绍并讨论有关建模电子邮件流量的文献。然后，我们解释了不同方法的优点和局限性以及它们的共识和分歧。最后，我们对RNN和长期短期记忆（LSTM）模型的性能进行了全面比较。