  Internet traffic in the real world is susceptible to various external and
internal factors which may abruptly change the normal traffic flow. Those
unexpected changes are considered outliers in traffic. However, deep sequence
models have been used to predict complex IP traffic, but their comparative
performance for anomalous traffic has not been studied extensively. In this
paper, we investigated and evaluated the performance of different deep sequence
models for anomalous traffic prediction. Several deep sequences models were
implemented to predict real traffic without and with outliers and show the
significance of outlier detection in real-world traffic prediction. First, two
different outlier detection techniques, such as the Three-Sigma rule and
Isolation Forest, were applied to identify the anomaly. Second, we adjusted
those abnormal data points using the Backward Filling technique before training
the model. Finally, the performance of different models was compared for
abnormal and adjusted traffic. LSTM_Encoder_Decoder (LSTM_En_De) is the best
prediction model in our experiment, reducing the deviation between actual and
predicted traffic by more than 11\% after adjusting the outliers. All other
models, including Recurrent Neural Network (RNN), Long Short-Term Memory
(LSTM), LSTM_En_De with Attention layer (LSTM_En_De_Atn), Gated Recurrent Unit
(GRU), show better prediction after replacing the outliers and decreasing
prediction error by more than 29%, 24%, 19%, and 10% respectively. Our
experimental results indicate that the outliers in the data can significantly
impact the quality of the prediction. Thus, outlier detection and mitigation
assist the deep sequence model in learning the general trend and making better
predictions.
