An Encoder-Decoder Deep Learning Approach for Multistep Service Traffic Prediction

In this research paper, we compare statistical time series with Deep Learning (DL) models. We propose an encoder-decoder DL approach for multi-step traffic prediction. We examined four encoder-decoder DL architectures i) Stacked LSTMs, ii) CNN-LSTMs, iii) Bidirectional LSTM and iv) an innovative Hybrid Unidirectional-Bidirectional LSTM. We conducted experiments using a TCP trace data set with a 5 minutes time-step. We predict the number of requests, the transmitted data and the duration of the sessions with multi-steps in a range of one to five steps, which corresponds to a time window that spans 25 minutes in total. The results show that the encoder-decoder architecture provides better accuracy results in regards to predicting the traffic and the duration of the sessions