Wavelet-Based Hybrid Machine Learning Model for Out-of-distribution Internet Traffic Prediction

Internet traffic prediction is a crucial component for the proactive management of self-organizing networks (SON) to ensure better Quality of Service (QoS) and Quality of Experience (QoE). Modern machine learning techniques have shown outstanding performance in analyzing and predicting complex internet traffic, which has non-linear and non-stationary characteristics. But most existing works assumed that model training and testing data came from independent and identical distribution (IID), which is hardly valid in actual scenarios. Also, they considered synthetic traffic datasets, which do not have enough random properties like real-world traffic. As a result, the model’s prediction accuracy measured using IID data samples is inconsistent with the accuracy of out-of-distribution (OOD) data instances. In this study, we investigated several machine learning models’ performances using four actual traffic datasets whose distribution is different than each other. The best prediction accuracy using IID samples was 96.4% which significantly dropped when we used OOD samples to evaluate the same model. Therefore, we proposed a hybrid machine learning model combining discrete wavelet transformation to decompose original data into several hierarchical components before feeding them into a prediction model. We train our hybrid models using these detail components as features that improve our best performance using IID samples by 1%. Also, it considerably reduces the best accuracy gap of conventional machine learning models in predicting IID and OOD samples by 3.5%, 6.7%, and 2.1%, respectively, for three OOD test sets.