Skip to main content

Table 3 RPEs for all models and all datasets

From: Modelling email traffic workloads with RNN and LSTM models

Prediction error (RPE %)
Category Technical University of Crete University of Peloponnese
Previous works Proposed models Previous works Proposed models
Incoming traffic Probability distribution RNN RNN LSTM Probability distribution RNN RNN LSTM
 Users incoming 21.5 13.9 6.22 5.64 16.7 9.20 3.29 3.33
 System incoming 20.8 2.1 1.97 2.04 23.00 7.00 9.96 6.08
Outgoing traffic
 Users incoming 14.7 9.4 2.28 1.34 29.60 13.70 4.96 5.22
 System incoming 10.00 5.30 2.25 2.22 20.40 4.40 2.83 1.38
Spam traffic
 Spam traffic 17.7 17.7 4.71 3.95 25.00 57.10 4.73 4.62
Category Murdoch University Liverpool John Moores University
Previous works Proposed models Previous works Proposed models
Incoming traffic Probability distribution RNN RNN LSTM Probability distribution RNN RNN LSTM
 Users incoming 32.70 14.20 0.37 0.19 8.4 4.2 1.35 1.09
 System incoming 9.30 4.20 0.29 0.25     
Outgoing traffic Spam traffic
 Users incoming 40.80 25.30 0.97 0.92 36.9 18.7 2.45 2.26
 System incoming 22.60 23.30 0.28 0.40