Out-of-Distribution Internet Traffic Prediction Generalization Using Deep Sequence Model

Efficient internet traffic prediction is very crucial for proactive network management. Unfortunately, it is a non-trivial task to design an effective prediction tool to capture the general pattern of complex, non-linear, and non-stationary real-world traffic. However, novel deep learning models have been developed for network traffic prediction, where they exhibit excellent performance. Most existing works assumed that training and testing data samples are independent and identically distributed (IID). But there is a high probability of having slightly or completely unknown data samples after model deployment, and the model should be able to predict them accurately. In this study, we show a comparative performance analysis among several deep sequence models using IID and out-of-distributed (OOD) samples. The prediction model average accuracy dropped significantly for OOD data samples compared to IID test data. Therefore, we proposed a hybrid architecture combining deep sequence models and discrete wavelet transformation (DWT), where models are trained using decomposed hierarchical components instead of original data. According to our experimental results, the hybrid model increases the prediction accuracy using IID samples by 2% compared to the standalone model. Also, the performance gap between IDD and OOD samples is reduced considerably by hybrid models, which indicates the outperformance of our proposed methodology to conventional deep learning models for both IDD and OOD test instances.